System and method for generating an alternative product recommendation

ABSTRACT

A method and system for automatically generating a self-updating naturally-reading narrative product summary including assertions about a selected product. In one embodiment, the system and method includes evaluating an existing narrative product summary, comparing an existing attribute name, attribute value, attribute unit, and assertion model, respectively, to a current attribute name, attribute value, attribute unit, and assertion model to determine if one of the comparisons shows a change. The system and method further determines a new attribute associated with the selected product, selects an alternative product, retrieves a new assertion model with assertions that describe the selected product and identify an alternative product in a natural manner. The system and method then generates a naturally-reading narrative product summary by combining the new attribute with the new retrieved assertion model, and by combining the selected alternative product with another retrieved assertion model to recommend the selected alternative product in the narrative.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part application of application Ser. No. 10/839,700, filed on May 6, 2004, which is a continuation-in-part application of application Ser. No. 10/430,679, filed May 7, 2003; the contents of both are incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is directed to systems and methods for generating product summaries. In particular, the present invention is directed to such systems and methods that allow automatic generation of product summaries together with alternative product recommendations that read naturally.

2. Description of Related Art

Many different models of products from many different manufacturers are generally available for each type or category of product. Typically, manufacturers of a particular category of product offer various models in their product line, each model targeting a particular group of users and/or meeting the specific needs of a market segment. For instance, manufacturers of automobiles, vacuum cleaners, cameras, computers, etc. all generally manufacture a variety of models of their products. In many instances, each model from each manufacturer and models from differing manufacturers have different features and/or attributes associated with the particular category of product.

For example, in the product category of vacuum cleaners, various models having different combinations of features and/or attributes are presently available. These features and/or attributes for vacuum cleaners include bag/bagless operation, motor power, attachments, exhaust filtration, price, etc. One particular model of a vacuum cleaner may have a bag to collect debris, a 6-ampere motor, without attachments or exhaust filtration, and be typically sold in the marketplace for $60 U.S. Another particular model of a vacuum cleaner may have a bagless compartment to collect debris, an 8 ampere motor, provided with attachments and a HEPA filtration, and be typically sold in the marketplace for $160 U.S. Of course, many other features and attributes may distinguish each of the vacuum cleaners that are available.

In another example, for the product category of digital cameras, features and/or attributes include optical and digital zoom capability, pixel count, the presence, type and size of a view screen, flash capacity, memory capacity, price, etc. One particular model of digital camera may have a 2× optical zoom, 2.1 megapixels, a flash, a 2 inch color view screen, a 32 Mb memory, and be typically sold in the market place for $200 U.S. Another particular model of digital camera may have a 3× digital zoom, 4 megapixels, a flash, a 3 inch color view screen, a 64 Mb memory and be typically sold in the market place for $400 U.S. Of course, many other features and attributes may distinguish each of the digital cameras that are available in the digital camera product category.

Moreover, the identity of the manufacturer of a particular product also impacts the desirability of the product. Generally, a product from a well known and respected manufacturer is more desirable to a consumer than a product from a lesser known manufacturer. Various manufacturers have, over time, become recognized as being market leaders with respect to particular category of products. For example, in the above noted vacuum cleaner product category, HOOVER™, EUREKA™ and DIRT DEVIL™ are examples of vacuum cleaner manufacturers considered to be market leaders. In the above noted product category of digital cameras, CANON™ and SONY™ are considered to be the market leaders. Each of the manufacturers generally provide various models with differing levels of features and performance that are targeted to specific consumer groups with particular use in mind.

The vast number of manufacturers and models available for each product category, and the disparity in features and/or attributes between the products of a product category, can make a consumer's purchasing decision very difficult. Companies such as CNET™ Networks, Inc., (hereinafter “CNET”), which operates www.cnet.com, provide information regarding consumer and technology oriented products such as electronics and computer products for buyers, sellers, and suppliers of technology, as well as any other interested user.

In addition to providing raw data and information, many different products in a particular product category are evaluated by editors of CNET for various features and/or attributes and rated on a scale of 1 through 10. Products that are evaluated to have higher quality and to provide superior value to consumers are rated higher than products of lesser quality and value. Moreover, the editors often provide written narrative product summaries that highlight various features of the particular product, and discuss strengths and weaknesses of the reviewed product in comparison to other comparable products.

Similarly, the products in a particular product category may be ranked within a product category based on their respective ratings, value, cost, features, and other characteristics determined by editors or by others. The products in a particular product category may be ordered within an evaluative scale of alternative products included in the particular product category.

The information and narrative product summaries provided by CNET and by others regarding various products in a product category may be used by consumers to facilitate potential purchase decisions. However, the process of rating the numerous products is time and labor intensive requiring trained editors or other personnel familiar with features and/or attributes of a product category to evaluate each of the products. Moreover, providing written narrative product summaries that highlight various features of the product, and discuss strengths and weaknesses of the reviewed product in comparison to other comparable products requires even more effort from the editors. Such written narrative product summaries requires significant time and resources of the editor, thereby correspondingly requiring significant capital expense to provide such written narrative product summaries by the editor.

Various automated systems have been developed to eliminate or substantially reduce the requirement for individual evaluation of each product. For instance, U.S. Pat. No. 5,731,991 to Kinra et al. discloses a system for evaluating a software product including an interface that receives product data relating to the software product, a first memory that stores the product data, and a second memory that stores a plurality of weighting values. The system also includes a processor that is coupled to the first memory and the second memory which applies the plurality of weighting values to the product data to generate at least one criterion score for the software product, each criterion score representing an evaluation of the software product.

U.S. Pat. No. 6,236,990 to Geller et al. discloses a system and method for assisting a user in selecting a product from multiple products that are grouped into categories. The reference discloses that information such as attributes about the products of each category, and questions related to the attributes, are received and stored. In addition, possible user's responses to the questions and weights associated with each possible response are also received and stored. Evaluation ratings for each of the attributes of each of the products are also received and stored. The reference discloses that the user selects a category and is provided with questions corresponding to the attributes of the products in the category selected. For each product in the category, a product score is calculated by summing the product of the weights of the responses by the evaluation ratings for that product. The results are displayed organized in rows and columns in the order of the product scores and weights. The reference further discloses that the user may change the weights, change categories, or obtain additional information about each product. In addition, the reference further discloses that the system allows attribution of the evaluation ratings, and may place an order for some or all products. However, the systems and methods of the above noted references merely provide ratings of plurality of products and do not provide systems and methods for generating narrative product summaries.

Other product review systems include systems that tally user votes on products, measure popularity of products by hits on a web page or by sales, or use collaborative filtering to measure which other products are commonly bought or also read about by customers who bought or read about a certain product. These product review systems invariably output numerical or discrete metrics such as by giving a product “three stars out of five” and do not provide any advice-bearing text summaries. Many e-commerce sites such as www.amazon.com and others employ such systems.

Other advice-text generating systems such as that used by The Motley Fool™ in the website www.fool.com generate evaluative text comments on stocks, takes into consideration dynamically updated data, and also produce real sentences. However, such systems evaluate and comment on all, and only on the same attributes of each product, regardless of the specific attributes of each item itself. Thus, these systems evaluate specific attributes of each product based on simple numerical threshold methods such as whether a quantitative value is above or below a certain point. In addition, such systems generate the same canned sentences for each item that falls below or above each threshold, so that the resultant sentences sound artificial and fabricated rather than product summaries that read naturally.

Thus, such product review systems provide reviews that are not in easily readable form that sound natural. Correspondingly, conventional systems and methods also fail to provide automatically generated product summaries that sound and read naturally to aid consumers in making purchase decisions. In addition, when a consumer researches a particular product using presently available on-line tools and resources, only information regarding the particular product being researched is provided. Although the consumer may desire to research and identify an alternative product similar to the particular product being researched, tools for facilitating identification of such alternative products are generally not available. For example, to obtain information regarding an alternative product in vendor websites, the consumer is typically required to display a listing of all of the products in a particular category, and to select an alternative product to thereby display detailed information regarding the selected product. However, this selection is done by the consumer without information as to whether the newly selected alternative product is comparable to the researched product.

Some vendor websites such as www.bestbuy.com provide a boxed window that may be entitled “Also Consider” or the like which identifies an alternative product for the consumer's consideration in a page displaying detailed information regarding a product that was selected by the consumer. The identified alternative product is implemented as a link, which may be selected by the consumer to obtain detailed information regarding the alternative product. However, the boxed window is implemented to merely identify products offered by the same manufacturer of the originally selected product and identifies products having nearly identical specifications that may only differ nominally, for example, by color of the trim, or to a product from the same manufacturer having increased capacity of a particular feature, for example, memory capacity. To obtain corresponding information regarding the alternative product, the provided link must be selected by the consumer, upon which detailed information regarding the original product researched is removed and detailed information regarding the new selected alternative product is displayed.

Further, some vendor web sites such as www.bestbuy.com noted above, allow the consumer to check a “Compare” box for two or more products in a listing of products, and to display product features of the selected products in a side-by-side manner thereby allowing the products selected to be compared with respect to various product specifications and features. However, the products compared are selected by the consumer and are not provided as alternative suggestions by vendor web sites. Thus, the compared products may be tailored for totally different target markets, which are generally not comparable with respect to each other. In addition, the comparative information provided is displayed as a tabulated listing, which is not easy to comprehend.

Additionally, as products age and technology advances, the ratings, value, rank, and other characteristics of a particular product may also change from the rating, value, and rank of the product when it was introduced to the market or initially reviewed. Ratings, value, rank, and other characteristics of the product may become stale or outdated as the market matures, additional products enter the market, competition increases, technological alternatives become available, and time passes. None of the conventional product review systems offer the flexibility of automatically generating a self-updating naturally-reading narrative product summary to address these consumer concerns.

Therefore, there exists an unfulfilled need for systems and methods for automatically generating narrative product summaries that sound and read naturally. In addition, there also exists an unfulfilled need for systems and methods for automatically generating narrative product summaries that are specifically tailored to the attributes discussed, and to the qualifications regarding the attributes. Furthermore, there also exists an unfulfilled need for systems and methods that automatically generate an alternative product recommendation. Moreover, there exists an unfulfilled need for such a system and method that provides such alternative product recommendation in a narrative that sounds and reads naturally.

SUMMARY OF THE INVENTION

In accordance with one aspect of the present invention, methods of automatically generating a naturally reading narrative product summary including assertions about a selected product are provided. In one embodiment, the method includes the steps of determining at least one attribute associated with the selected product, the attribute including an attribute name, an attribute value, and/or an attribute unit, selecting a comparable product, retrieving assertion models defining forms in which assertions can be manifested to describe the selected product and the selected alternative product in a natural manner, and generating a naturally reading narrative by combining the attribute with a retrieved assertion model and combining the selected comparable product with another retrieved assertion model to recommend the selected comparable product in the narrative.

In another embodiment, the method includes the steps of determining at least one attribute associated with the selected product, the attribute including an attribute name, an attribute value, and/or an attribute unit, selecting an alternative product, retrieving assertion models defining forms in which an assertion can be manifested to describe the product and to recommend an alternative product in a natural manner, and generating a naturally reading narrative by combining the at least one attribute and the selected alternative product with the retrieved assertion models such that the generated narrative includes a recommendation of the selected alternative product.

In accordance with another aspect of the present invention, a product summary generator for automatically generating a naturally reading narrative product summary including assertions about a selected product is provided. In accordance with one embodiment, the product summary generator includes a product attribute module adapted to determine at least one attribute associated with the selected product, the attribute including at least one of an attribute name, an attribute value, and/or an attribute unit and further adapted to select a comparable product, an assertion model module adapted to retrieve assertion models that define forms in which assertions can be manifested to describe the selected product and the selected comparable product in a natural manner, and a summary generation module adapted to combine the attribute with a retrieved assertion model to describe the selected product in the narrative and to combine the comparable product with another retrieved assertion model to recommend the selected comparable product in the narrative.

In accordance with another embodiment, the product summary generator includes a product attribute module adapted to determine at least one attribute associated with the selected product, the attribute including at least one of an attribute name, an attribute value, and/or an attribute unit, an alternative product selection module adapted to select an alternative product, an assertion model module adapted to retrieve assertion models that define forms in which an assertion can be manifested to describe the selected product and to recommend the selected alternative product, in a natural manner, and a summary generation module adapted to generate a naturally reading narrative by combining the attribute and the selected alternative product with the retrieved assertion models so that the narrative includes a recommendation of the selected alternative product.

In accordance with another aspect of the present invention, a method of automatically generating a naturally reading narrative product summary including assertions about a selected product is provided. In one embodiment, the method includes the steps of determining at least one attribute associated with the selected product, the at least one attribute including at least one of an attribute name, an attribute value, and an attribute unit, retrieving assertion models defining forms in which an assertion can be manifested to describe the selected product, and to recommend an alternative product in a natural manner, receiving a plurality of bids from manufacturers that indicate an amount of compensation each manufacturer is willing to pay to have at least one of the manufacturer's product to be recommended as the alternative product, selecting the alternative product based on the plurality of bids, and generating a naturally reading narrative by combining the at least one attribute and the selected alternative product with the retrieved assertion models such that the generated narrative includes a recommendation of the selected alternative product.

Another aspect of the present invention provides a method of automatically generating a self-updating naturally reading narrative product summary. In one embodiment, the method includes the steps of determining if a narrative product summary exists for a selected product, and if a narrative product summary exists for the selected product, the method reconciles the existing narrative product summary into an existing attribute associated with the selected product, the existing attribute including at least one of an existing attribute name, an existing attribute value, or an existing attribute unit. The method then resolves defined forms in which an assertion describes the selected product to an existing assertion model and compares at least one of the existing attribute name, the existing attribute value, the existing attribute unit, and the existing assertion model, respectively, to at least one of a current attribute name, a current attribute value, a current attribute unit, and a current assertion model to determine if at least one of the comparisons shows a change in the attribute name, the attribute value, the attribute unit, or the assertion model. If a change is evident, the method further determines at least one new attribute associated with the selected product, the at least one new attribute including at least one of a changed attribute name, a changed attribute value, or a changed attribute unit, and retrieves a new assertion model defining changed forms in which a new assertion is used to describe the product in a natural manner. The method then generates an updated naturally reading narrative by combining the new attribute with the retrieved new assertion model.

The method and system of the present invention may also include a notification to a user that an update has taken place of the self-updating naturally-reading narrative product summary. The notification may be automatic, and the user may be notified by mail, by electronic mail, by an RSS feed, or the like.

Additionally, the present invention provides a method of selecting an alternative product to the selected product that may be the basis for a comparison of characteristics, ratings, value, ranking, and other features, qualities, or traits of the products. The present invention provides a method of generating a naturally-reading narrative product summary for the alternative product as well.

In the above regard, in another embodiment, the manufacturer may bid to be the only manufacturer, or one of a few manufacturers, whose products are included in a limited sub-pool of alternative products, which are eligible for selection for recommendation. The best and most favorable alternative product in the sub-pool would then be selected for recommendation. If more than one manufacturer is represented with products in the limited sub-pool for the same category of products, an optional weighting factor based on the highest bid may be provided so that the products from the highest bidder may be selected. Of course, the selection of the alternative product may be based on other business consideration, for example, if a revenue model other than bidding is utilized for payment.

These and other advantages and features of the present invention will become more apparent from the following detailed description of the preferred embodiments of the present invention when viewed in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of a product review generator in accordance with one example embodiment of the present invention.

FIG. 2A is a schematic illustration showing the relationships between elements of a product summary that is generated by the product review generator of FIG. 1.

FIG. 2B is the product summary of FIG. 2A which has been instantiated to provide a product summary that reads naturally.

FIG. 3A is a schematic illustration of an example assertion template in accordance with the present invention.

FIG. 3B is the assertion template of FIG. 3A which has been instantiated.

FIG. 4 is a flow diagram illustrating one example method in accordance with the present invention.

FIG. 5 is a flow diagram illustrating another example method in accordance with the present invention.

FIG. 6 is a flow diagram illustrating an example method of updating a narrative product description based upon a change in a component of the existing attribute.

FIG. 7 is a flow diagram illustrating an example method of updating a narrative product description based upon an alternative product recommendation.

GLOSSARY OF TERMS

-   Additive rule: a rule for composing a secondary attribute, which     indicates that if the primary attribute is instantiated more than     once, then the values will be concatenated. See also: secondary     attribute, override rule and optimizing rule. -   Assertion model: a set of assertion templates, which define various     forms in which an assertion can manifest itself as a sentence. -   Assertion template: One of several grammatical patterns, together     with additional field names such as the key attribute for the     template, an optional excluded feature, and an optional requisite     feature. The sentence pattern can be a list of “snippets.” See also:     snippet, assertion, key attribute, excluded feature, requisite     feature. -   Assertion: a point or premise of information, fact, or opinion being     made by any number of possible sentences or fragments which express     that point; the common state-of-affairs depicted by possible     variants of an assertion. -   Attribute name-value pair: A matched attribute name and attribute     value, also called a feature. E.g. “3-megapixel resolution.” May     also include the units of the attribute name. -   Attribute: any defined attribute of a product or product category,     whether intrinsic or extrinsic, whether primary or secondary, is     considered an attribute. -   Connotation: Within an assertion template, an internal flag     indicating whether the tone of the sentence is positive, negative,     or neutral. This flag is set internally to match the connotation of     the value of the key attribute of the assertion template. It is then     used to determine whether a continuous transition or contrasting     transition is needed when placing the sentence into a paragraph. -   Excluded feature: In a secondary attribute composition rule, or in     an assertion template, the attribute name-value pair which, if     present in the product, should cause the building of the attribute     or assertion to fail. -   Extrinsic attribute: an attribute which is not inherently a part of     the product but is dependent on external factors such as the     marketplace, human judgment about the product, human purposes     satisfied by the product, etc. -   Feature: an attribute name-value-unit combination. For example,     “resolution” is an attribute name, “3 megapixel” is a value-unit     pair, and “3 megapixel resolution” is a feature. -   Field name: any field name of an attribute, vocabulary, product, or     assertion. Numerous field names can be referenced in the input files     accepted by the product summary generator. -   Importance rating: a rating of how important an attribute is within     a product category. For example, a scale of 1-10 can be used. These     ratings can be modified by a usage scenario. -   Intrinsic attribute: an attribute not dependent on comparison with     things or events outside the product itself. See also: extrinsic     attribute. -   Key attribute: Attribute that is very important to a particular     product-category and at least partially defines performance or     desirability of a product in the product category. The key attribute     of a category is used in special ways, such as to give context to a     product in introductory remarks, for example, calling a camera a     “3-megapixel camera”. Each assertion template within an assertion     model has its own key attribute. Within an assertion template, any     references to applicable field names are taken to refer to this key     attribute. -   Optimizing rule: a rule for building a secondary attribute which     means that if the primary attribute is instantiated more than once,     then the new value will be an override if, and only if, the     previously instantiated value is non-optimal; otherwise the old     value can be retained. If more than one optimal value is     instantiated, they can be additive with respect to each other. If at     least one optimal value is instantiated, non-optimal values are     over-written and no further non-optimal values will be admitted.     Implies that results fields for secondary attribute composition rule     contain some result value(s) marked with an appended asterisk (*) to     indicate that they are optimal, while other values are not. See     also, secondary attribute, override rule and additive rule. -   Ordinality: indication of in what order assertions should be placed     within the same thematic paragraph. Assertions, which are tied in     their ordinality, are randomly ordered with respect to one another. -   Override rule: a rule for building a secondary attribute, which     means that if there are successive redundant functions for defining     a secondary attribute, each instance will override the previous one.     It should be rare that an editor would have redundant override     rules, however. Normally, when defining multiple rules for building     the same attribute, one would set this flag to either additive or     optimizing. -   Primary attribute: An attribute that is inputted to the system,     deriving either from our spec table, price feed, or product record.     Distinguished from secondary attributes, which are created     dynamically by the system. -   Primary scenario: the usage scenario, which is selected as the     default assignment for a particular product. E.g. a camera best     suited for professional photography. -   Product summary: a body of text intended to concisely state several     insights or observations about a particular product. See also:     theme, assertion. -   Property: a primary intrinsic attribute. In other words, an     attribute not dependent on any contextual elements outside the     product itself, therefore independent of the market, and independent     of human judgment and use-case scenarios. See also: attribute,     intrinsic attribute, primary attribute. -   Resident functions: the built-in functions for building secondary     attributes. The most commonly used examples are: sum, product,     quotient, absolute value, positive threshold, negative threshold,     sub-string, and test. -   Required feature: a feature that is required for triggering of a     secondary attribute rule or an assertion-to-sentence transformation. -   Scalarization: the ordering of values within a value range of an     attribute so as to form an evaluative scale, e.g. ordering batteries     from more favorable to less favorable in the order of Lithium, NiMH,     NiCad, Alkaline. The scalarization is done according to an     evaluative metric—meaning the question of which aspect, element, or     quality of the attribute was chosen for the scalarization. For     example, batteries were ordered above probably according to their     charge-life and lack of “memory,” but if they were to be ordered     according to ease-of-availability and/or being least-expensive, the     scalarization would be different because a different evaluative     metric had been adopted. -   Scenario: see usage scenario. -   Scenario glosses: phrases established for each scenario to describe     the activities, settings and environments, etc. pertaining to each     usage scenario. -   Secondary attribute: an attribute that is formed by applying a     function to one or more pre-existing attributes. Distinguished from     primary attributes, which are inputted from a file and derived from     a function. Both primary and secondary attributes may be referenced     in the rule for forming a secondary attribute. A collection of     resident functions is support by the product summary generator for     creating secondary attributes. -   Sentence: A complete, grammatical sentence formed by the generator.     Every sentence is also called a variant of its assertion model. -   Severity value: A value expressing how seriously good or how     seriously bad it is for a product to have a certain attribute value.     A very low or a very high severity is indicative of an attribute of     the product that is strong interest to most consumers. -   Snippet: a single element of an assertion template representing a     sentence fragment, and including free-form text, word replacement     sets, and/or field name. See also: assertion template, word     replacement set, field name. -   Theme: The concept, which unifies multiple assertions together. See     also: assertion. -   Units: the unit-of-measure for an attribute, e.g. megapixels. -   Usage scenario: An alternative set of importance ratings and     scalarizations reflecting a different purpose, point-of-view, or     intended usage of a type of product. Usage scenarios are inheritable     according to the taxonomical structure of product categories, and     they are additive so that they can be combined. -   Variant: a fully-formed sentence that is derived from an assertion     model. After an assertion template is selected and word-replacements     and value look-ups are performed, the resulting, complete sentence     is a “variant” of the assertion template. See also: assertion,     assertion model, assertion template. -   Word replacement set: The simpler way of varying phraseology on a     small scale within an assertion model, whereby simple lists of     alternative, fully-interchangeable words are indicated as choices.     See also: assertion, assertion model, assertion template.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic illustration of a product summary generator 10 for generating narrative product summaries in accordance with one embodiment of the present invention. It will be evident from the discussion below that the preferred embodiment of the present invention provides a product summary generator which generates product summaries that sound and read naturally so that it can be easily understood by the reader and information presented therein can be used to aid in purchasing decisions. In addition, it will also be evident that the present invention may also be utilized to provide a recommendation for an alternative product.

For clarity purposes, the present invention is also described below as applied to generating a narrative product summary with an alternative product recommendation for digital cameras. However, it should be evident that the present invention may be readily used to generate product summaries with alternative product recommendations for any products of any product category, and application to digital cameras is merely provided as one example. Moreover, various terminologies are utilized to describe the preferred embodiment of the present invention, definitions of various terms are provided in the GLOSSARY OF TERMS section set forth above to facilitate complete comprehension of the preferred embodiment.

In accordance with the illustrated embodiment of the present invention, the product summary generator 10 is provided with a central processing unit 12 (hereinafter “CPU”) which is adapted to control and/or facilitate functions of various modules of the product summary generator 10 as described in detail below. It should be initially noted that the product summary generator 10 of FIG. 1 may be implemented with any type of hardware and software, and may be a pre-programmed general purpose computing device. For example, the product summary generator 10 may be implemented using a server, a personal computer, a portable computer, a thin client, a hand held device, a wireless phone, or any combination of such devices. The product summary generator 10 may be a single device at a single location or multiple devices at a single, or multiple, locations that are connected together using any appropriate communication protocols over any communication medium such as electric cable, fiber optic cable, any other cable, or in a wireless manner using radio frequency, infrared, or other technologies.

It should also be noted that the product summary generator 10 in accordance with one embodiment of the present invention is illustrated and discussed herein as having a plurality of modules or sub-modules which perform particular functions. It should be understood that these modules are merely schematically illustrated based on their function for clarity purposes' only, and do not necessary represent specific hardware or software. In this regard, these modules and/or sub-modules may be hardware and/or software-implemented and substantially perform the particular functions explained. Moreover, two or more of these modules may be combined together within the product summary generator 10, or divided into more modules based on the particular function desired. Thus, the present invention as schematically embodied in FIG. 1 should not be construed to limit the product summary generator 10 of the present invention.

In the illustrated embodiment, the product summary generator 10 is connected to a network 14 that allows publishing and remote access to the product summary generator 10. Moreover, the network 14 allows the product summary generator 10 to access various sources of information such as the manufacturer's database 15 and/or the vendor's database 16 to obtain various information regarding the products of a products category, and includes memory 17 to allow storage of such information. Such databases may be publicly accessible databases that are maintained by manufactures and/or vendors of such products being reviewed. The product summary generator 10 may be provided with an engine 18 that is adapted to access the manufacturer's database 15 and/or the vendor's database 16 via the network 14 to retrieve such information for storage into the memory 17. Moreover, the memory 17 may be any appropriate type of memory for storage of data and may be based on optical, magnetic or flash memory technologies.

The network 14 may be any type of communications channel, such as the Internet, a local area network (LAN), a wide area network (WAN), direct computer connections, and may be accomplished in a wireless manner using radio frequency, infrared, or other technologies, using any type of communication hardware and protocols. Of course, in, other implementations, such information may be directly provided to the memory 17 product summary generator 10 without accessing manufacturer's database 15 and/or the vendor's database 16 via the network 14. The specific details of the above referenced devices and technologies are well known and thus, omitted herein.

The product summary generator 10 as shown in FIG. 1 includes a product attribute module 20 that is adapted to create and build various groups of information that may be used generate the naturally reading narrative product summaries with the alternative product recommendation. In particular, the product attribute module 20 is adapted to build scenario glosses, general category information, and product attribute information as described in further detail below. In addition, the product attribute module 20 is further adapted to generate price analysis information.

The product summary generator 10 also includes a secondary attribute module 30 that is adapted to build secondary attributes by reading the desired parameters, and is further adapted to utilize appropriate functions to generate the secondary attributes. In this regard, a rule definition database 36 with functions stored therein is provided so that functions can be retrieved and executed by the product summary generator 10.

An assertion model module 40 that is also provided in the product summary generator 10 is adapted to build assertion models by assigning a theme to each model, reading every template to the appropriate model, and by reading snippets to the appropriate template. In this regard, the assertion model module 40 is provided with an assertion model database 42, assertion template database 45, and snippets database 47.

Furthermore, the product summary generator 10 of the illustrated embodiment is also provided with a summary generation module 50 that is adapted to generate reserved themes, generate supplermental themes, format and assemble the generated sentences, and display the product summary. The various functions and features of each module of the product summary generator 10 are described in further detail below.

As previously noted, the preferred embodiment of the present invention provides a product summary generator 10 which generates product summaries that sound and read naturally so that they can be easily understood by the reader and information presented therein can be used to aid in purchasing decisions. Moreover, the preferred embodiment, the product summaries generated by the product summary generator 10 include a theme in which an alternative product is recommended.

In one embodiment of the present invention, the product summary generator 10 includes a summary generation module 50 adapted to evaluate an existing narrative product summary including reconciling the existing narrative product summary into an existing attribute associated with the selected product, the existing attribute including at least one of an existing attribute name, an existing attribute value, or an existing attribute unit. The summary generation module 50 is further adapted to resolve forms in the existing narrative product summary to an existing assertion model.

The product summary generator 10 also includes an engine 18 adapted to compare at least one of the existing attribute name, the existing attribute value, the existing attribute unit, and the existing assertion model, respectively, to at least one of a current attribute name, a current attribute value, a current attribute unit, and a current assertion model to determine if at least one of the comparisons shows a change in the attribute name, the attribute value, the attribute unit, or the assertion model. The existing attribute value of the selected product may be ordered within an evaluative scale of alternative products or otherwise ordered in a product category.

The product summary generator 10 also includes a product attribute module 20 adapted to obtain at least one new attribute associated with the selected product, the at least one new attribute including at least one of a changed attribute name, a changed attribute value, or a changed attribute unit. Further, the product summary generator 10 includes an alternative product selection module 60 adapted to select an alternative product, an assertion model module 40 adapted to retrieve new assertion models that define forms in which assertions describe the selected product and in which assertions describe the selected alternative product in a natural manner. The changed attribute value of the selected product may also be ordered within an evaluative scale of alternative products.

Also, the summary generation module 50 is further adapted to combine the at least one new attribute with a new retrieved assertion model to describe the selected product in the narrative and to combine the selected alternative product with another retrieved assertion model to recommend the selected alternative product in the narrative.

As noted, product summaries are a body of text intended to concisely state several insights or observations about a particular product. Such product summaries typically include one or more paragraphs, each preferably having a plurality of sentences which provide facts, information and opinion regarding a particular product. Such product summaries generated by the product summary generator 10 of the present invention are substantively analogous to product reviews that are written by editors which are familiar to those of ordinary skill in the art except that product summaries are automatically generated by the product summary generator 10 in accordance with the present invention. Further, the generated alternative product recommendation provided in the product summaries is one or more sentences that identify and briefly discuss, an alternative product to the product reviewed in the generated other themes of the product summary.

FIG. 2A shows a schematic structure of an example product summary 100, which illustrates the relationships between various elements of the product summary 100 which are generated by the various modules of the product summary generator 10 of FIG. 1. A corresponding portion of example product summary 110 is shown in FIG. 2B in which a digital camera is reviewed. As shown in FIG. 2B, the product summary that is generated by the product summary generator 10 sounds and reads naturally as if an informed individual actually wrote the review so that it can be easily understood by the reader, and information presented therein can be readily used to aid in purchasing decisions.

As can be seen in FIG. 2A, each product summary 100 is provided with a plurality of themes 102, each theme setting forth the intended purpose of one or more sentences and unifying such sentences in the generated product summary. For example, an “Introduction” theme may be provided to address the product being reviewed in general terms, and “Noteworthy features” theme may be provided to address the specific features of the product being reviewed that stand out.

Each theme includes one or more assertion models, which are complete sets of assertion templates, which define various forms in which the assertion can manifest itself as a sentence. Assertions are a point or premise of information, fact, or opinion being made by any number of possible sentences, which express that point. Assertion templates are sentence patterns, together with additional field names that form a sentence when the field names are filled. Each of the assertion templates have snippets associated thereto, a snippet being a single element of the assertion template and representing a sentence fragment, and including free-form text, word replacement sets, and/or field name, examples of which are described in further detail below. Moreover, transitional terms may be provided between the assertion models or at other locations of the product summaries as also described in further detail below.

FIG. 3A shows an example an assertion template 150 having various snippets that are filled in to generate a sentence that reads naturally. As shown, the illustrated example assertion plate 159 may be:

-   -   “This [key attribute: value units] [generic long singular name]         is suited for [primary scenario gloss: activity] and at [street         price], is [price-point text]-priced for a [scenario gloss:         type] [generic short singular name].”

The assertion template 150 of FIG. 3A may then be instantiated into assertion template 151 as shown in FIG. 3B by filling in the various snippets to read:

-   -   “This 3 megapixel digital camera is suited for taking casual         snap-shots, and at $184, is low-priced for a general consumer         camera.”

Of course, it should be evident that the above product summary 100 as shown in FIG. 2A is merely a schematic example and different numbers of themes, assertion models, assertion templates, snippets, and/or transitional terms may be provided that is tailored to the product summary being generated by the product summary generator 10 in accordance with the present invention. Moreover, it should also be evident that assertion template 150 as shown in FIG. 3A is also merely one example and other various templates may be provided and used. For example, assertion, template for use within an alternative product recommendation theme may be provided which may be instantiated using various snippets that identify an alternative product and one or more attributes associated thereto.

The discussion below describes how the product summary generator 10 and the modules thereof as shown in FIG. 1 may be utilized to generate product summaries such as that shown in FIG. 2B. As previously noted, the examples of digital cameras are utilized extensively to facilitate comprehension of various aspects of the present invention. However, it should be kept in mind that the present invention as described herein is equally applicable for generating product summaries of various other products in product categories.

Referring to FIG. 1, the product attribute module 20 is adapted to create the primary feature comparison space. Most of the elements utilized by the product attribute module 20 are either information that is read directly from an input file (for example, memory 17) or database (for example, databases 15 and/or 16), or information that is derived from mathematical functions thereof.

The product attribute module 20 is adapted to build scenario glosses, which are phrases established for each scenario to describe the activities, settings and environments, etc. pertaining to each usage scenario. The product attribute module 20 pulls into memory 17 of the product summary generator 10, certain semantic elements of a scenario, such as the type of user for a particular category of products (e.g. “for photo enthusiasts”), and the adjectival form of the scenario for attachment to the noun (e.g. “semi-pro” in “this is a semi-pro camera.”).

In the illustrated embodiment of the product summary generator 10, the product attribute module 20 is also adapted to build general category information regarding products for which product summaries are generated. The product attribute module 20 reads into memory 17, information that is preferably generic to the entire category of products, i.e. information that is not at a product-specific level. For example, the product attribute module obtains primary attribute name and vocabulary, including the short and long, singular and plural forms of the attribute name; where applicable.

In particular, the product attribute module 20 is also adapted to obtain importance ratings of primary attributes that designate which attributes are important in a given category of products. Of course, such importance ratings of primary attributes are designated in a generic sense, and not from the point of view of a specific scenario or application of the product. The product attribute module 20 obtains the primary attribute units, if applicable. For example, in the category of digital cameras, resolution is measured in “megapixels” which serves as the units of the primary attribute of resolution. Such unit information may be obtained in any appropriate manner, including obtaining such unit information from an input file, or by mining it from other sources.

The product attribute module 20 is also adapted to pull into memory 17, any adjustments to attribute importance ratings (expressed, for example, on a scale of 1-10) for each usage scenario so that the importance ratings can be adjusted depending on the usage scenario. In accordance with one implementation, the adjustments may be “distance-to-goal” meaning that the importance ratings of attributes are adjusted by a certain desired percentage upward toward a maximum, or downward toward a minimum. For example, a +0.40 adjustment would mean increasing the importance rating 40% of the attribute toward 0.100% from where it stands initially. If the importance rating was initially a 5, this adjustment would increase the importance rating to a 7, and if the initial importance rating was a 7.5, this adjustment would increase the importance rating to an 8.5. Conversely, a −0.40 adjustment would mean decreasing the rating 40% toward zero from the initial rating. Thus, the initial rating of 10 would be lowered to a rating of 6, and an initial rating of 5 would be lowered to a rating of 3.

Similarly, other attribute values may be ranked, rated, and characterized in a range of existing attribute values. The range of attribute values may be scalarized so that a continuum of values is represented with products possessing attributes or characteristics that may be represented as values within the range. Products in a class of similar or peer products possess a relative attribute value that may be used to compare a selected product to other products with similar attributes within the peer class.

The above-described adjustments to the importance rating of the attribute of a product category allow scenarios to be combined iteratively. If one scenario for digital cameras is for “fast-action use” and another is for “water resistant use”, the two scenarios can be combined. For instance, if both scenarios increase the importance rating of the product attribute of ruggedness by 50%, then, if the rating of the ruggedness attribute was initially 6, the importance rating would be increased to 9. This would be due to the fact that the initial importance rating of 6 would be first increased to 8, and then, an increase from 8 to 9.

In addition, the product attribute module 20 also obtains language expressing the fact that a product possesses the attribute, whether the possession has a positive connotation or a negative connotation. For example, in a category of products that require batteries, the neutral term may be “uses”, but the negative equivalent may be “requires”. Correspondingly, in evaluating a product with a single battery, a sentence may be generated that states that the product “uses a single lithium battery” as opposed to “requires 4 AA alkaline batteries.”

The attributes may also be flagged as to whether a given primary attribute is a key attribute. In this regard, editors who initially set up the product summary generator 10 to allow automatic generation of product summaries may mark whether one or more of the e primary attributes are considered to be definitive of a product category so as to be designated “key” attributes. An ordinary-language usage test of this is to ask which attributes are commonly used in definite descriptions of products. In the case where the category of products being evaluated is digital cameras, “resolution” would be a key attribute because of how common it is in the literature to see phrases such as “the latest 5-megapixel camera from . . . ” The fact that “megapixel” is a unit of measure for “resolution” also indicates that resolution is a key attribute for the digital camera product category.

Furthermore, the product attribute module 20 of the product summary generator 10 is also adapted to obtain product attribute information and associated para-data for the products in a product category. In particular, the product attribute module 20 obtains the actual attribute values associated with the attributes of a particular product in a product category from any appropriate source. For example, in the example of digital cameras, an attribute value for the attribute of “resolution” would be 4.2 megapixels in its full native (non-interpolated) resolution for a particular camera. Such information would be associated with the particular camera.

The engine 18 may obtain product information from the manufacturer's database 15 and/or the vendor's database 16 via the network 14. The engine 18 may be provided with search engines having a crawler such as a robot, bot or automated site searcher. The crawler may automatically crawl from servers to servers having such information to gather the appropriate information. Of course, details of such search engines are well known in the art and need not be described in detail herein. Moreover, the product information may be provided to the product summary generator 10 in any other appropriate manner. For example, the information may be gathered by a device other than the product summary generator 10 of the present invention, and provided to the memory 17 of the product summary generator 10 for further processing and generation of the product summaries.

In addition, in the illustrated implementation of the product summary generator 10, the product attribute module 20 is also adapted to obtain primary attribute ranks for each of the products in the product category and read them into memory 17. The primary attribute ranks indicate the rank of each product relative to the other products in the product category based on each of the attributes, and may be determined in any appropriate manner. For example, the conventional method of percentile ranks may be used to obtain the primary attribute ranks. Alternatively, more sophisticated methods may be used to determine the primary attribute ranks. For example, competitive index for the attributes of the products may be determined using the method as set forth in U.S. patent application Ser. No. 10/265,189 filed Oct. 7, 2002 entitled SYSTEM AND METHOD FOR RATING PLURAL PRODUCTS, the contents of which are incorporated herein by reference.

As products age and technology advances, the ratings, value, rank, and other attributes of a particular product may also change from the rating, value, and rank of the product when it was introduced to the market or initially reviewed. Ratings, value, rank, and other characteristics of the product may become stale or outdated as the market matures, additional products enter the market, competition increases, technological alternatives become available, and time passes. Attribute importance ratings may also change over time as the marketplace changes, costs change, and alternatives in the market become more evident. The product attribute module 20 is adapted to obtain new attributes associated with the products, including attribute names, attribute values, and attribute units.

Attribute values of the selected product that may be ranked, rated, and characterized in a range of existing attribute values may correspondingly change in relation to the other alternative products in the peer class of products. For example, if the selected product was first-to-market with an important feature, the selected product may initially have the top-rated attribute value with regard to that feature. If a similar product within the peer class of products later introduced a similar feature with improved technical specifications, the position of the attribute value of the selected product will drop in the scalarized range of attribute values because the alternative product includes an improved attribute. The range of attribute values may be scalarized to represent a continuum of values for products possessing the attribute under consideration. If the selected product is “outdone” by an alternative product in the peer class of products, the attribute value of the selected product will correspondingly drop. Similarly, if a change in price occurs with regard to the selected product or an alternative product within the peer class of products, a corresponding change in the position of the selected product or the alternative product within the scalarized range of attribute values may similarly occur.

If competitive indexes are determined and used for attribute ranks as set forth in the '189 application noted above, the product attribute module 20 may also be used to flag a product or attribute of the product for verification when competitive index for the attribute of the selected product deviates a predetermined amount from an average competitive index for the attribute of the category of products. This allows checking to see if there are corrupt data. For example, if a very inexpensive camera is listed as having 1/5000th second shutter speed, which is among the fastest on the market, such an attribute would be considered a “noteworthy feature” for discussion in the generated product summary. However, such a feature would likely be too good to be true and may be based on corrupt data with a typographical error in which the actual shutter speed is 1/500th second, not 1/5000^(th) second. Thus, by examining the competitive index when the competitive index of an attribute is very far below, or very far above what is typical of its price point, the product summary generator 10 may be adapted to refrain from making a comment about it within the generated product summary, and instead, flag the attribute for human verification.

Once the primary attribute ranks are determined, the product attribute module 20 calculates primary attribute severity values for each of the products. The severity value essentially expresses how seriously good or how seriously bad it is for a product to have a certain attribute value. In one implementation, severity values may be set to range from 0.00 to 1.00, where a very low or a very high severity is indicative of an attribute of the product that is strong interest to most consumers. The severity values are used in the present implementation because the primary attribute ranks do not provide an indication of which feature(s) stand out as exceptionally noteworthy or weak for the product on the whole which should be discussed in the generated product summary, even if competitive index described in the '189 application are used.

For example, a large-screen console television (hereinafter “TV”) that is heavier than almost any other similar TVs by a few ounces will have a very low percentile rank or competitive index. However, because weight is not an important feature for console style televisions, a small amount of extra weight is not a severe condition with respect to the console TV, even though it is of very low rank compared to other lighter TVs. Conversely, another TV might be a few ounces lighter than all the other TVs, but this is hardly a very noteworthy feature.

Preferably, the severity value is a function of both the attribute rank (percentile, competitive, or other) and the importance rating of the primary attribute. In one implementation, the function utilizes an inflection algorithm that causes an inflection, whereby attribute values that are highly important (i.e. high importance rating) are pushed closer toward 0.00 when their attribute rank is low, but are held closer toward 1.00 when their attribute rank is high, while at the same time, this effect of pushing the severity value is not obtained when the importance rating is low.

The above-described inflection may be obtained by any number of numerical methods so as to accomplish the above-described effect. An example method is shown by the following formulas:

When Rank≧0.50 then Severity=Rank*(1−Degree+(Degree*Importance)).

and

When Rank<0.50 then Severity=Rank*(1−Degree+(Degree*(1−Importance)));

where Degree is between 0.01 and 0.99. The degree of the inflection can be controlled by setting the Degree to a predetermined number. A higher predetermined number for Degree would produce a more radical inflection between attributes of high and low importance.

For example, when Degree=0.50 (the default setting), the formula comes out as follows:

When Rank≧0.50 then Severity=Rank*(0.50+(0.50*Importance))

and

When Rank<0.50 then Severity=Rank*(0.50+(0.50*(1−Importance)))

Numerous example calculations of severity values is provided below with Degree=0.50 to illustrate the operation of the inflection algorithm of the present embodiment.

If importance is .90 and Rank is .90; Severity = .90 * (.50 + .45) = .90 * .95 = .855 If importance is .60 and Rank is .90; Severity = .90 * (.50 + .30) = .90 * .80 = .720 If importance is .30 and Rank is .90; Severity = .90 * (.50 + .15) = .90 * .65 = .585 If importance is .90 and Rank is .60; Severity = .60 * (.50 + .45) = .60 * .95 = .570 If importance is .60 and Rank is .60; Severity = .60 * (.50 + .30) = .60 * .80 = .480 If importance is .30 and Rank is .60; Severity = .60 * (.50 + .15) = .60 * .65 = .390 If importance is .30 and Rank is .30; Severity = .30 * (.50 + .35) = .30 * .85 = .255 If importance is .60 and Rank is .30; Severity = .30 * (.50 + .20) = .30 * .70 = .210 If importance is .90 and Rank is .30; Severity = .30 * (.50 + .05) = .30 * .55 = .165

In the examples set forth above, it should be evident that the most noteworthy cases, which are the first and last examples, have the highest and lowest severity values. The attribute having the highest severity value would correspond to the best attribute of the product being reviewed while the attribute having the lowest severity value would correspond to the worst attribute of the product being reviewed. Importance rating alone, or attribute rank alone, would not make these two cases stand out as being noteworthy for discussion. Mere multiplication of importance rating and attribute rank together would also not make these to cases stand out either. Thus, inflection algorithm such as that described above is desirable to produce the desired effect. The above-described method allows the product summary generator 10 to determine the best and worst attributes so that naturally reading narratives can be generated regarding those attributes of the product being reviewed in the product summary 100.

Once the severity values of the primary attributes for a particular product are calculated, for example, in the manner described above, the primary attributes are placed in order of their severity values. In addition, their placement within the plurality of severity values are calculated so as to provide the placement-by-severity, for example, a primary attribute may be designated as being the 3rd most severe, etc. Furthermore, in addition to calculating placement-by-severity as described, the product attribute module 20 also calculates placement order according to a differential in the percentile rank or competitive index from the average percentile rank/competitive index of products in the same scenario.

The product attribute module 20 further determines an Average-Rank-Near-Price margin which allows identification of other products in the product category that are close enough in cost to be cost-comparative to a given product being reviewed. The near-price margin may be set to be around 10% so that products of the product category having prices within 10% of the price of the product being reviewed are identified. Of course, the near-price margin may be configurable so that it can be set at any desired level. The identified products within the near-price margin are taken as a subset and retrievably stored in memory 17 for later use, for example, in highlighting strengths or weaknesses of the product reviewed in the narrative product summary, or in generating an alternative product recommendation in the narrative product summary. In addition, the average attribute rank among the identified products, whether the attribute rank is based on percentile rank or competitive index, is calculated for each primary attribute.

In addition, the product attribute module 20 further determines an Average-Price-Near-Rank margin which allows identification of other products in the product category that are close enough in rank to be compared to a given product being reviewed. Again, the near-rank margin is configurable but may be approximately 5%. Thus, the near-rank margin allows determination of which products of a product category have a comparable value to the product being reviewed for a given primary attribute. The average price of the identified products is also determined. In the present implementation, the above described determination is preferably performed for every primary attribute of every product, and is retrievably stored in memory 17 for later use. This information can again, be used to highlight strengths or weaknesses of the product reviewed in the narrative product summary, or in generating an alternative product recommendation in the narrative product summary.

In one implementation, the product attribute module 20 of the product summary generator 10 is adapted to characterize the typical feature value of products costing nearly the same (among comparable products and/or among the entire category), and also adapted to characterize the typical price of products having nearly the same feature (among comparable products and/or among the entire category). To ensure that the generated product summary contains statements regarding value of the product when they are relevant, one or more threshold measures may be provided to determine when they are sufficiently noteworthy to merit a mention. This may be attained by the product attribute module 20 by determining two derivative measures, Value-For-Price and Price-For-Value, from the Average-Rank-Near-Price margin that was described previously above.

Value-For-Price may be defined as the Average-Rank-Near-Price minus the rank of the same attribute value of the product being reviewed. The Value-For. Price may be expressed as the difference value or be expressed as a percentage of the attribute valve of the product being reviewed. For example, if the Average-Rank-Near-Price for a camera's resolution is 0.44 and the rank of the camera's resolution is 0.36, then the camera's resolution Value-For-Price may be expressed as being 0.08. This means that 0.08 competitive points of surplus value with respect to resolution is provided by the average camera having a similar price to the camera being reviewed. Stated in another manner, this means that the product being reviewed is at a deficit of 0.08 competitive points. When the Value-For-Price is above a certain configurable threshold, the product summary generator 10 may be triggered to generate statements referring to the Average-Rank-Near-Price of that product's attribute. The triggering mechanism may further be provided as a follow-up to identification of the most noteworthy features on the basis of their severity as discussed previously above.

Finally, the product attribute module 20 of the illustrated embodiment also generates price analysis information, which can be used to characterize a reviewed product as being “expensive” or “low-priced”, etc. This is attained by initially determining the global mean and standard deviation of prices for all products in a product category, regardless of scenario. In addition, global high, low, and median prices are also determined which also preferably includes the median high and median low. The price analysis is repeated with each subset of products that falls into each scenario.

The secondary attribute module 30 in accordance with the present implementation is adapted to build secondary attributes, which are attributes that are formed by applying a function to one or more pre-existing attributes. Initially, the secondary attribute module 30 reads into memory 17, all of the functions and secondary attribute definition rules described below from the functions database 36 that have been manually edited by an editor(s) that initially sets up the product summary generator 10, and also validates the syntax and integrity of the secondary attribute definition rules. These secondary attribute definition rules are then used as tools by the secondary attribute module 30 to build the secondary attributes.

In the above regard, the secondary attribute module 30 supports various generic functions, which may be provided in the functions database 36 that are standard to most mathematical and/or statistical analysis software packages. These functions may include:

AbsDiff = absolute value of the difference of two arguments. AbsPercDiff = absolute value of percentage difference of first argument relative to second argument AvgOf = statistical mean of all arguments Diff = first argument minus second argument GreaterOf = greatest value of all arguments LesserOf = least value of all arguments MedianOf = statistical median of all arguments PercDiff = percentage difference of first argument relative to second argument PercOf = first argument measured as a percentage of second argument Product = result of multiplying all arguments Quotient = result of dividing first argument by second argument Sum = result of adding arguments together Test = first argument names attribute to be tested; subsequent argument pairs provide test case values, with their respective resulting values in square brackets

Furthermore, the secondary attribute module supports 30 various specific functions that may be tailored expressly for facilitating generation of secondary attributes. These specific functions may include:

HigherRankOf = highest of corresponding competitive index of all arguments LowerRankOf = least of corresponding competitive index of all arguments PosThreshold = first argument names attribute to be tested; subsequent argument pairs provide descending positive thresholds, each followed by the resulting value in square brackets NegThreshold = first argument names attribute to be tested; subsequent argument pairs provide ascending negative thresholds, each followed by the resulting value in square brackets

Of course, the above functions are provided as examples only and other functions may also be provided in other implementations of the secondary attribute module 30.

There may be multiple functions that are appropriate for the generation of a secondary attribute. Consequently, in the illustrated implementation, the multiple functions are all executed in sequence for the same secondary attribute. This multiplicity provides more than one way for the product summary generator 10 to attempt to generate the same secondary attribute, which is especially important in an advice-giving environment where there are multiple different ways that a product or a feature thereof can be useful or valuable, or not useful or valuable.

For example, in the product category of digital cameras, “suggested use” for digital cameras may be considered a secondary attribute. Clearly there are many multiple tests, and multiple possible outcomes, for how to derive this value. If the camera has a waterproof case, a suggested use might be for snorkeling. Alternatively, if the digital camera has a very high optical zoom, then “outdoor sightseeing photography” might be appropriate, etc. These multiple definitions can be defined successively.

Each particular secondary attribute, however, has only one main function associated with it in the present implementation. For more complicated secondary attributes, these functions can effectively be nested by making reference to previously created secondary attributes within a new secondary attribute.

The secondary attribute module 30 of the product summary generator 10 calls an appropriate function, retrieves and validates the arguments, observes value dimension specified, and performs string conversion as necessary. “Value dimension” refers to whether the desired value of the attribute in question is the attribute value, the attribute rank, the placement-by-severity, the placement-by-differential-rank, the average-rank-near-price, or the average-price-near-rank, as described in further detail below. All subsequent references within the secondary attribute assume this value dimension which defaults to the attribute value itself. In many cases, the value dimension should be converted from a string to an integer or real number, and spelled-out numbers may be converted to digits, etc.

The secondary attribute can possibly require the presence of, or absence of, a particular value of another attribute. The secondary attribute module 30 of the present implementation is adapted to test for such a requirement so that if the test fails, a predetermined Fail-Value may be used. For example, for the secondary attribute “recommended minimum flash card size”, the function may stipulate that the presence of a flash card slot in the camera is required. The fail value may be “N.A.” indicating that flash card slot is not available.

The secondary attribute module 30 may also be used to reevaluate and update product parameters. If product attributes change over the lifecycle of a product, the secondary attribute module 30 may monitor the changes and cause the assertion model module 40 and product attribute module 20 to generate an updated narrative by combining new assertions and new secondary attributes.

The secondary attribute module 30 executes one or more functions described above to generate a secondary attribute, the appropriate function being called with the validated parameters. When the functions are executed and are about to return a value, the secondary attribute module 30 decides how to write the value back into the attribute record associated with the secondary attribute in view of the fact that there are often more than one function used to generate the secondary attribute. In this regard, rules may be provided in the function database 36 that may be accessed by the secondary attribute module 30. Such rules may include:

-   -   Override Rule: means if the primary attribute is instantiated         more than once, then subsequent instantiations will overwrite         previous ones.     -   Additive Rule: means if the primary attribute is instantiated         more than once, then the values will be concatenated, as         comma-delimited strings.     -   Optimize Rule: means if the primary attribute is instantiated         more than once, then the new value will be an override if, and         only if, the previously instantiated value is non-optimal;         otherwise the old value will be retained. If more than one         optimal value is instantiated, they will be additive with         respect to each other. If at least one optimal value is         instantiated, all non-optimal values are over-written and no         further non-optimal values will be admitted.

The “override,” “additive,” and “optimize” rules are each mutually exclusive with the others. If the rule definition is additive, then the secondary attribute module 30 checks to determine whether pre-existing values exist. If they do, then the newly derived value is linked together onto the list of multiple value strings in the attribute record, and the attribute is marked as having multiple values. If there are no pre-existing values, the new value alone is written as a singular value for the generated secondary attribute. If the rule definition is for an override, then the secondary attribute module 30 replaces any existing attribute value with the newly derived value.

If the rule is for optimizing, then the secondary attribute module 30 first determines whether the newly derived value is optimal or not. If the newly derived value is optimal, the secondary attribute module determines whether there are pre-existing values, which are non-optimal. If there are non-optimal pre-existing values, these values are deleted. Then, the secondary attribute module 30 determines if any pre-existing optimal values remain. If there are, the new value is linked onto the list and the attribute is flagged as having multiple values. If the new value itself is non-optimal, the secondary attribute module 30 determines if there are pre-existing values that are optimal.

If there are pre-existing values that are optimal, the new value is discarded, and the rule definition exits the function while leaving the attribute value unchanged. If there are no pre-existing optimal values, the secondary attribute module 30 checks if there are pre-existing non-optimal values. If there are, the secondary attribute module 30 links the new value and flags the attribute as having multiple values. If there are no pre-existing values, then the secondary attribute module 30 writes the new value as the singular value of the attribute. This optimize rule can thus, be used for creating several parallel primary attributes which have divergent means of instantiating the same secondary property.

In many cases, secondary attribute rules contain stipulations of positive connotation or negative connotation pertaining to the resulting attribute value. These connotations are mapped into the product based on the specific value it derives from the secondary attribute rule definitions. For example, on the secondary attribute for digital cameras of “largest print size recommended,” the values of ‘5″×7″’ or above, may be assigned a positive connotation, while those of ‘2″×3″’ or lower, may be assigned a negative connotation, with print sizes in between being neutral.

The secondary attribute module 30 may be used by the editors or by others for inputting the information, functions, and/or rules that are applicable for generating the secondary attributes. Any appropriate file(s) may be used such as a flat file, or files of a relational database. The data may be stored in any appropriate format such as tab-delimited format or various proprietary database formats. For example, the following chart sets forth file header fields for such files with a description for each header in the adjacent column. The header fields may be as follows:

HEADER FIELD DESCRIPTION Property Name [flags] The internal name of the property, with any flags (as to whether it is an override property, additive, or optimizing) Display Name <features to The display name - this name can reference the names of other mention> properties if placed in angle brackets. Assumption <features to mention> An assumption - dependent clause that explains assumption on which the building of this property is based - can also mention other properties in angle brackets. Requisite feature [Value] If another property must have a specific value in order for this secondary property to be triggered. If this condition fails, the fail value will be generated (see below). Excluded Feature [Value] If another property must NOT have a specific value in order for this secondary property to be triggered. If this condition fails, the fail value will be generated (see below). Determiner The grammatical determiner that would normally be appropriate to speaking of this property (e.g. “the”, “a”, “an”, “some”, “one”). Copula The grammatical copula used to attach this property to its value in a phrase, e.g. usually “is”, “has”, or in less frequent cases, “includes”, “supports,” etc. Function [optional alternative value The logical or mathematical operator on which the generation of dimension] the secondary property is to be based, e.g. Product, Quotient, PosThreshold, etc. In square brackets is the optional value dimension, for example, “rank” would mean to use the attributes' ranks instead of their values. Units Any units, if applicable, for this attribute, e.g. inches, megabytes, etc. Fail Value The value to generate if the function fails to produce a result, or if the tests for requisite or excluded feature are not passed. First Argument [result], Additional The first argument to be passed to the function, and optionally, Arguments [results] further arguments; in some cases, result values need to accompany each argument, depending on the function chosen.

Referring again to the product summary generator 10 shown in FIG. 1, the assertion model module 40 is adapted to build assertion models by initially assigning a theme to each of the assertion models, reading every assertion template from the assertion templates database 45 associated to the appropriate assertion model, and reading every snippet from the snippets database 47 to the appropriate assertion template.

Every assertion model is designated by the assertion model module 40 of the product summary generator 10 as belonging to one or more themes. As previously described, themes typically correspond to the paragraphs of the finished product summaries such as that shown in FIG. 2B. In this regard, several reserved themes may be provided in accordance with one implementation of the present invention. As previously noted relative to FIG. 2A, an “Introduction” theme may be provided which gives a general overview or a non-evaluative description of the product. A “Noteworthy features” theme may be provided which highlights positive features of the product being reviewed relative to comparable products. A “Weaker features” theme may be provided which points out negative features of the product being reviewed relative to comparable products. In addition, a “Value” theme may be provided to discuss the “bang-for-the-buck” the particular product being reviewed offers in comparison to other comparable products. Furthermore, “Suitability” theme may be provided which identifies what scenarios, use-cases, or types of users are the best fit for the product in question. Finally, an “Alternative Product Recommendation” theme may be provided which identifies and briefly discusses an alternative product that the user may consider.

Initial “Introduction”, “Noteworthy features”, “Weaker features”, “Value”, “Suitability,” and “Alternative Product” themes assigned by assertion model module 40 may be appropriate when the product is introduced to the marketplace, but as products age, technology advances, and marketplace alternatives become more prevalent, the features that help define the content of the themes may no longer be applicable, or may be less applicable than they were at the time the product was introduced. For example, the initial assertion models assigned may be quite different from assertion models that would be assigned to the product if it were being initially evaluated at this time. Further, the attribute name, the attribute value, the attribute unit, and additional characteristics of the product may no longer include the same values, rank, or other attributes of a particular product that would be used to generate the initial themes when the product was introduced to the market or when the product was most recently reviewed.

Ratings, value, rank, and other characteristics of the product may become stale or outdated as the market matures, additional products enter the market, competition increases, technological alternatives become available, and time passes. Attribute importance ratings may also change over time as the marketplace changes, costs change, and alternatives in the market become more evident. The product attribute module 20 is adapted to obtain new attributes associated with the products, including attribute names, attribute values, and attribute units. Further, the assertion model module 40 retrieves new assertion models that define forms in which new assertions now describe the product. By automatically adapting to changes in product attributes and assertion models used to characterize a product, the system and method of the present invention provides a self-updating naturally-reading narrative product summary.

For example, as shown schematically in the flow diagram 600 of FIG. 6, the product summary generator 10 may automatically update a naturally-reading narrative product summary by determining if a product summary exists in step 602. If, in step 606 the system determines that no product summary exists for the selected product, then in step 608 the system creates a product summary, and the process stops. However, if in step 606 the system determines that a product summary already exists for the selected product, then in step 610 the system reconciles the existing product summary into existing attributes associated with the selected product. The existing attributes include existing attribute names, existing attribute values, or existing attribute units.

In step 614, the summary generation module 50 resolves forms in the existing narrative product summary to existing assertion models.

In step 618, an engine 18 compares the existing attribute names, the existing attribute values, the existing attribute units, and the existing assertion models, respectively, to current attribute names, current attribute values, a current attribute units, and current assertion models to determine in step 622 if at least one of the comparisons shows a change in the attribute name, the attribute value, the attribute unit, or the assertion model.

If, in step 622 the system determines that there was a change in a component of the attribute, then in step 630 the product attribute module 20 obtains the new attributes associated with the selected product. The new attributes include at least changed attribute names, changed attribute values, or changed attribute units.

Once the new attributes are obtained, in step 634 the assertion model module 40 retrieves new assertion models that define forms in which assertions describe the selected product in a natural manner.

After the new assertion models that define forms in which assertions describe the selected product are retrieved, in step 638 the summary generation module combines the new attributes with the new retrieved assertion models to describe the selected product in the narrative.

Optionally, the method and system of the present invention may also include a notification to a user that an update has taken place of the self-updating naturally-reading narrative product summary. The notification may be automatic, and the user may be notified by mail, by electronic mail, by an RSS feed, and the like.

The aforementioned themes may all have resident procedures for defining their content. However, their core content may be supplemented by additional assertions defined in the configurable assertion models for each product category. This may be attained by merely assigning a reserved theme name to an assertion so that an editor thereby defines it as supplementary to that reserved theme.

Of course, supplemental themes may be provided by the assertion model module 40 as well, such supplemental themes being typically displayed in the generated product summary after the reserved themes. Such supplemental themes may address any aspect of the product category and may be appropriately named. An example of a supplemental theme for digital cameras may be “On storing pictures” theme in which assertion templates regarding storage capacity of the digital camera is addressed. In particular, the theme may identify and discuss whether a particular digital camera being reviewed is provided with a storage card or if such card must be purchased separately, and what the recommended card size for storing a satisfactory amount of pictures is, and the like.

As previously noted, the assertion model module 40 reads every assertion template from the assertion templates database 45 that correspond to the appropriate assertion models in the assertion models database 45, each record in the assertion model defining an assertion template. As schematically shown in FIG. 2A, an assertion template includes one or more snippets from the snippets database 47, and revolves around one particular key attribute which may be a primary attribute or a secondary attribute.

It should be noted that a plural number of assertion templates are typically assigned to the same assertion name, but often have different key attributes. The fact that these assertion templates are assigned to the same assertion name generally indicates these assertion templates are all making essentially the same point about a product being reviewed, except that the assertions are being made in different ways.

In addition, as also previously noted, the assertion model module 40 reads the snippets from the snippets database 47 to the appropriate assertion template, each snippet being a short phrase, usually not capable of standing as a sentence by itself. In the present implementation, each of the snippets in the snippets database 47 may contain three kinds of content: static text, minor variants, or references to attributes or parameters. Static text never changes within the assertion template from the way it is entered by the editor when the product summary generator 10 was set up. Minor variants are inter-changeable phrases which are randomly chosen from a list input by the editor by the product summary generator 10. For example, minor variants may be the phrases “However,” and “On the other hand,” which are substantially equivalent in meaning to one another. References to attributes or parameters are lookups of any attributes, whether primary or secondary, or to parameters of the key attribute, such as its name, its units, its vocabulary elements, and the like.

In a manner similar to the secondary attribute module 30 described above, any appropriate file(s) may used to input information regarding the assertion models, for example, via a flat file, or files of a relational database. The data may be stored in any appropriate format such as tab-delimited format or various proprietary database formats. The following chart sets forth file header fields for such files with a description for each header in the adjacent column. The header fields may be as follows:

HEADER FIELD DESCRIPTION Assertion Name The internal name of the assertion Key Attribute The attribute to which it should be assumed all subsequent references apply, within this template, unless stated otherwise. Theme The theme under which this assertion falls. Excluded Feature Name [Excluded If another property must NOT have a specific value in order for Value] this secondary property to be triggered. If this condition fails, the fail value will be generated (see below). Connotation Attribute The attribute from which this assertion derives its connotation. Can be negative, positive, or neutral. Contiguous transition Verbiage to use if this assertion maintains the same connotation as the sentence preceding it. Contrasting transition Verbiage to use if this assertion bears a different connotation from the sentence preceding it. Template Elements <Variables in Template elements each have any combination of static text, angle brackets> variables that can name any attribute or elements of attributes, and minor phrase variants in square brackets (e.g. [“barely”, “hardly”] would result in random selection of “barely” or “hardly” in the same position in the sentence).

Referring again to the illustrated embodiment of the product summary generator 10 of FIG. 1, the summary generation module 50 is adapted to generate reserved themes, generate supplemental themes, format the generated sentences, assemble bullet items into sentences, and display the product summary such as that shown in FIG. 2B with an alternative product recommendation therein. The summary generation module 50 generates the reserved themes utilizing the key attributes and the primary scenario, as well as the price analysis. These elements are excellent ways to introduce a product to a potential buyer who is browsing various products of a product category. The example assertion template 150 as shown in FIG. 3A is appropriate for the introduction theme, which when instantiated, may read:

-   -   “This 3 megapixel digital camera is suited for taking casual         snap-shots, and at $184, is low-priced for a general consumer         camera.”

The summary generation module 50 of the product summary generator 10 of the illustrated embodiment also generates the noteworthy features theme by initially determining the noteworthy features, whether the features are noteworthy for being positive or negative. To identify noteworthy features, the summary generation module 50 first examines the placement-by-severity in which the primary or secondary attributes are ordered based on their corresponding severity values, and selects the attributes having the highest severity values, but only if the severity values are above a predetermined high-severity threshold. Preferably, the high-severity threshold is a number that is configurable for the product summary generator 10. The purpose of the high-severity threshold is that, if an attribute is not very severe, then the attribute is not worth generating a sentence about in the product summary. The summary generation module 50 may be adapted to select a predetermined number of highest severity attributes that are above the high-severity threshold so that sentences are generated for those attributes in the product summary.

If the predetermined number of such attributes is not present, the summary generation module 50 examines the placement-by-differential-rank in which attributes are ordered by their attribute ranks. This allows determination of which attributes of the product is markedly superior than most of the other products in the product category sharing its primary scenario and costing around the same price. Again, a predetermined threshold may be implemented so that if no attributes lie above this threshold, the summary generation module 60 will not generate any sentences in the product summary highlighting any noteworthy features. Appropriately, the summary generation module 50 of the product summary generator 10 may find no noteworthy features at all for a particular product being reviewed since some products are truly not noteworthy.

In addition, the editor may have found additional secondary attributes during setup of the product summary generator 10 which, under certain conditions, can be considered noteworthy. These secondary attributes may be added on a product category basis. For example, in digital cameras, the editor may decide that any digital camera having a water-tight case for use while snorkeling is noteworthy for that attribute since it is generally not a common attribute among digital cameras. Since this attribute is unlikely to be highly important among digital cameras generally, it is unlikely to pass the tests within the resident procedures for finding “noteworthy” features, and thus, would need to be added as a supplemental assertion.

Of course, in most instances, it would not be sufficient to merely find and mention a noteworthy feature since the primary object of the product summary generator 10 is to provide user friendly information regarding the product being reviewed. In order to be relevant and useful to most prospective buyers of a product, the noteworthy feature must be placed in some sort of context, or otherwise explained. The summary generation module 50 should thus provide an indication of whether the feature is noteworthy in an absolute sense, or in a relative sense. Some features are noteworthy with respect to entire category of products, and so are “absolutely” noteworthy, but are not noteworthy relative to one or more attributes such as product price, or even the primary scenario.

For example, there may be a digital camera on the market with the primary scenario of “semi-professional photography,” selling for around $8,000 and having an 11-megapixel resolution. This resolution is, in the current market, very high and ranks second highest of over two hundred models on the market. Obviously, this feature is noteworthy in the “absolute” sense. However, relative to its price, this is not noteworthy since $8,000 for a digital camera is very expensive, and the few cameras having comparable resolution are priced similarly. Moreover, among the small subset of digital cameras in the digital camera category that are indicated as “semi-professional”, this resolution is again, not particularly noteworthy. By contrast, a compact camera costing only $150 with 3× optical zoom is not noteworthy in the absolute sense since 3× optical zoom is middle-of-the-road. However, considering the compact camera's size and its very low price, it is noteworthy in the relative sense.

Each of these various types of noteworthiness requires a different contextualization statement in the product summary that is generated. For a feature that is noteworthy only in the absolute sense, the summary generation module 50 of the product summary generator 10 may be adapted to generate a sentence that would say something like:

-   -   “This is superior to most cameras, but is to be expected for a         camera of this price.”

For a feature that is noteworthy only relative to its primary scenario, the summary generation module 50 of the product summary generator 10 may be adapted to generate a sentence that would say something like:

-   -   “This is not particularly better than most other cameras at a         similar price, but it is rare among compact cameras.”

For a feature that is noteworthy for the product's price, the summary generation module 50 may be adapted to generate a sentence that mentions the typical price of comparable products boasting this feature. For example, for a digital camera costing $300 with very high resolution, the summary generation module 50 may be adapted to generate a sentence that would say something like:

-   -   “The typical cost of cameras having comparable resolution is         around $640.”

The finished assertion regarding a noteworthy feature may be referred to as the “ranked attribute description” because it is addressing one of the predetermined number of most noteworthy attributes, and is describing the noteworthy attribute by the virtue of which the product being received the attribute ranking. For instance, the “aspect” of a shutter speed that made it noteworthy may be the fact that the shutter speed is “faster” than that of “most cameras costing the same.”

The summary generation module 50 further generates the weaker or negative features theme which is the logical converse of noteworthy features, with the exception of some of the explanatory elements. Because of the fact that the product summary generator 10 is primarily for the purpose of assisting a person in making a purchasing decision, when an important feature is weak or actually negative, it would be desirable for the product summary generator 10 to direct the user, for the sake of comparison, to a comparable product that is stronger or more positive for that particular feature.

Similarly, when the summary generation module 50 generates an updated theme that is indicative of a change in status or rank of the selected product, the product summary generator 10 may direct the user to additional comparable products that are stronger or more positive than the new characteristics of the considered product.

If the user is not concurrently evaluating products, the method and system of the present invention may notify a user that an update has taken place of the self-updating naturally-reading narrative product summary. The notification may be automatic, and the user may be notified by mail, by electronic mail, by an RSS feed, and the like.

A comparable product would typically share the same primary scenario, cost about the same, and be similar with respect to the key feature of the product category. For instance, for digital cameras, comparable resolution may be required to be a comparable product since resolution is the most important feature. Thus, if a compact digital camera is being reviewed, another compact digital camera would be noted as being comparable if it falls within the near-price margin, and has a resolution that falls within the near-rank margin. Once all the comparable products for the reviewed product are determined, the comparable product having the highest value rating (such as percentile rank or competitive index) for each attribute is stored. This comparable product may be referred to in the weaker features theme, and may later even be identified as an alternative product recommendation theme.

In the above regard, in cases where the reviewed product is found to be weak in some noteworthy sense, a corresponding comparable product having the highest value rating for the attribute for which the reviewed product is weak, may be referenced within the product summary. Thus, in the present example, if a 3-megapixel compact camera costing $320 is weak in its optical zoom and has only 2×, the product summary generator 10 would identify the best comparative product in respect to zoom, and after mentioning that this particular feature is weak, follow-up with a sentence that says something like:

-   -   “For example, the HP 725xi, a 3.1 megapixel compact camera at         around $329, has 4× optical zoom.”

The summary generation module 50 further generates the value theme by contextualizing the value rating of the product, whether the value rating is based on percentile rank or competitive index as described in U.S. patent application Ser. No. 10/265,189 incorporated by reference above. This means explaining whether the value rating, if noteworthy (either bad or good), is driven more by price, or driven more by features. For example, a digital camera with better-than-average features but that is overpriced, and a camera with a slightly-below-average price but far-below-average features, may have potentially the same value rating. However, these digital cameras both have the same value rating for different reasons. In the former case, the summary generation module 50 may generate a sentence that would say something like:

-   -   “Despite this camera's excellent features, the relatively high         price makes it a poor value for the dollar.”

In the latter case, the summary generation module 50 may generate a sentence that would say something like:

-   -   “Despite costing less than most cameras on the market, the         inferior features of this camera cause it to be a poor value for         the money.”

The summary generation module 50 may be triggered to address the value of the product being reviewed in any appropriate manner. In one implementation, this may be attained by utilizing the Value-For-Price as determined by the product attribute module 20 as discussed previously.

Another element of the value theme is that the summary generation module 50 finds the highest or lowest placement-by-rank-differential. This is useful because a product that is generally mediocre in value, neither very good nor bad, may nonetheless be exceptionally good or bad with respect to one important attribute, and the person may like to know this fact. For example, a camera with average value may have a very fast shutter speed for its price. Analysis of this fact can be used by the summary generation module 50 to generate the comment:

-   -   “Overall this camera has average value for the money, based on         features. However you would typically pay about $80 more to get         a camera having this fast a shutter speed.”

Furthermore, supplemental assertions that are category specific may be offered by the editor. For example, ‘Bluetooth’ wireless ability may be an expensive feature for manufactures to provide it in a digital camera. If a camera is provided with such a feature, the price of the camera will reflect this fact and may count as a good value. However, the editor may recognize that this is not a very important feature, and not of very much usefulness to most users of the product category. In this case, a secondary attribute could be introduced to state this fact where applicable, and the summary generation module 50 may generate a sentence that would say something like:

-   -   “Although this camera has relatively good value for its         features, a substantial part of the price you are paying         accounts for the Bluetooth ability, which only a small number of         users will find useful.”

Moreover, the summary generation module 50 may be adapted to generate the suitability theme through contextualization of the primary scenario. The primary scenario is the scenario in which a product scores the highest in its value rating as described in the '189 application or a scenario that has been manually set as to be the primary scenario by the editor. The various scenario glosses which are phrases established for each scenario to describe the activities, settings and environments, etc. pertaining to that scenario, are easily assembled into a sentence by the summary generation module 50. In addition, of the reserved themes described above, suitability is most likely to have supplemental themes added to it, since use cases are so specific to the product category in question.

For example, for digital cameras, the editors that set up the product summary generator 10 of the present invention may desired to characterize which digital cameras as being suitable for sports-action photography, for instance, digital cameras having fast shutter speeds and short delays between shots. This would of course be a secondary attribute, which would become the basis of an assertion assigned to the “Suitability” theme.

In many cases, an attribute will be unknown to most users, or the meaning of its various values will not be of clear relevance in their minds. In such cases, an optional, generic explanation can be attached to any sentence referencing such an attribute. For example, for shutter speed, the summary generation module 50 may be adapted to generate a sentence that would say something like:

-   -   “Faster shutter speeds allow you to ‘freeze’ the action in a         snapshot, and also help cancel the ‘shake effect’ of an unsteady         hand when shooting with a high powered zoom.”

The product summary generator 10 may also be provided with a configurable verbosity setting in which the editor and/or the user can select the length of discussion provided by the generated product summary so that the product summary may be really short, really long, or in between. Based on the verbosity setting of the product summary generator 10, the generic explanation may be either included or omitted.

Many assertions described above read perfectly fine when taken in isolation. However, when these assertions are assembled into a product summary, they can combine in ways that are entirely unnatural sounding. Therefore, the product summary generator 10 of the illustrated embodiment is adapted to ensure that the product summary generated reads naturally and in a narrative manner. In particular, the summary generation module 50 may refer to the same thing or attach the same sort of predicate twice in a row, which is not natural in human language. This may be resolved by merging two assertions into one.

For example, in providing discussions regarding noteworthy features, two features may be found that are both noteworthy in an absolute sense, but not in a relative sense. In the example of digital cameras, the two features may be resolution and shutter speed. Independently, the two assertions might be formed by the summary generation module 50 as follows:

-   -   “Noteworthy features: Has 5.1 megapixel resolution, which is         considerably better than most cameras, although it is to be         expected in a camera costing this much. Has 1/5000th second         shutter speed, which is much faster than that of most cameras,         although it is to be expected in a camera costing this much.”

Note that in the above example, the two assertions would read fine and naturally when read independently. However, when the two assertions are read in succession, the two assertions sound robotic. Therefore, the product summary generator 10 may be adapted to monitor the result type of the description generated relative to the ranked attribute as discussed above relative to the noteworthy features theme. When the product summary generator 10 receives the same result type twice in a row, it may be adapted to adjust the grammar accordingly to generate a sentence that would say something like:

-   -   “Noteworthy features: Has 5.1 megapixel resolution and 1/5000th         second shutter speed, both of which are superior to most cameras         on the market, but to be expected in a camera costing this         much.”

As can be appreciated, by monitoring repetition of references or predicates, an assertion that merges two different assertions while reading naturally can be generated by the product summary generator 10. Other forms of repetition may include, but are not limited to: mention of two features that are mutually exclusive (exceptional) for the scenario in question; mentioning that a particular competitive product outperforms the current model in two attributes at the same time, etc. Furthermore, any number of grammatical forms may be used to merge or connect these assertions. For example, providing the word “also” in between the two independent assertions, or completely merging the assertions into a single sentence as described above, may be used to merge or connect the assertions.

Many secondary attribute values have negative or positive connotations associated with them. Furthermore, every assertion template may optionally designate the attribute from which it derives its own connotation, for example, the key attribute of the template. When generating a product summary, the product summary generator 10 is adapted to monitor the connotation of the preceding and succeeding assertion templates from which each assertion is being generated. It notes whether the transition between the two is contiguous or contrasting, i.e. whether it is the same, or not the same connotation. In such an implementation, a snippet for both conditions, contiguous and contrasting may be provided within each assertion template which can be appropriately used based on the desired connotation. The snippets can be as simple as just the text string, “Also, . . . ” for contiguous transition, and the string “However, . . . ” for contrasting transition. Of course, other snippets may be provided as well that has more structure such as the phrase “On the other hand, when it comes just to [attribute short singular name], . . . ”

Because the product summary generator 10 of the present invention is primarily designed for the purpose of facilitating the evaluation of products in one or more product categories, the flow between negative and positive remarks is important to the naturalness of the text. Tracking the connotation context and using the appropriate contiguous and contrasting transitional phrases as described above provides this ability and results in generation of product summaries that sound natural and narrative as if the product summaries were written by a person such as an editor.

In fact, the lack of such transitions can actually make an otherwise natural text seem contradictory. For example, consider the following paragraph that has been generated with proper transitions:

-   -   “Overall, this camera's features are average for this price         point. On the other hand, when it comes to resolution, you would         typically have to pay around 22% more to get a general-purpose         camera that matches its 3.2 megapixels.”

The same paragraph would read very differently if transitions were disregarded:

-   -   “Overall, this camera's features are average for this price         point. You would typically have to pay around 22% more to get a         general-purpose camera that matches its 3.2 megapixels.”

It should be very clear that in the latter version, the second sentence seems to contradict the first and is likely to be dissonant in the mind of the reader. In the former version, the second seems to supplement and complete the first one.

As described above relative to FIG. 2A, the product summary generated by the product summary generator 10 of the illustrated embodiment is provided with a plurality of themes such as “Introduction” and “Noteworthy Features”, each theme setting forth the intended purpose of one or more sentences and unifying such sentences in the generated product summary. In this regard, the summary generation module 50 may be implemented to generate a product summary with a separate theme such as “Alternative Product Recommendation” theme that allows generation of a narrative sentence or sentences that recommend the alternative product to the user for consideration.

The alternative product recommended may be the same as the comparable product discussed above, which may be identified for the consumer in the narrative of various themes to facilitate assessment of the selected product that is reviewed in the product summary. However, the alternative product may also differ from the comparative product in that the actual product recommended may not be really comparable to the selected product which is reviewed by the product summary generated. For example, an alternative product that is outside the near-price margin may be recommended. In another example, an alternative product that is outside the near-rank margin may be recommended. In yet another example, an alternative product that has a primary scenario that is different from the selected product reviewed may be recommended. Such alternative products would generally not be considered comparable to the original selected product, but nonetheless, they may be recommended as described in further detail below.

As shown in FIGS. 2A and 2B, the sentence providing the alternative recommendation may be located within the generated product summary 100, after the weaker or negative features theme, for example. Thus, in another example of digital cameras, if a 3-megapixel compact camera costing $320 is weak in its optical zoom and has only 2×, the summary generation module 50 may be adapted mention this weakness in the weaker features theme, and then, provide a recommendation regarding an alternative product in the alternative recommendation theme (for example, Theme 3) thereafter with a sentence that says something like:

-   -   “You may want to consider the HP 725xi, a 3.1 megapixel compact         camera at around $329 which provides similar features but has 4×         optical zoom.”         Of course, the sentence recommending the alternative product may         be provided in a different location within the product summary         in other implementations.

The alternative recommendation theme may be implemented in a similar manner as the other themes of the product summary as discussed previously. The assertion models module 40 may retrieve an appropriate assertion model from the assertion model database 42 created for the alternative recommendation theme to generate the alternative recommendation. In particular, an assertion model with a set of assertion templates implemented as one or more sentence patterns may be retrieved from the assertion templates database 45 and used in conjunction associated snippets of the snippets database 47 to generate one or more sentences that recommend the alternative product in a naturally sounding manner.

Referring again to FIG. 1, the product summary generator 10 in accordance with the illustrated embodiment includes an alternative product selection module 60 which identifies and/or selects the alternative product to recommend from a plurality of candidate alternative products that are stored in the alternative products database 64. The alternative product selection module 60 may select the alternative product recommended based any appropriate criterion. Thus, the alternative products database 64 may include attributes, severity values for each of the attributes, and other determined values, as well as other information regarding each of the candidate alternative products in a particular product category. In addition, the alternative products selection module 60 may further specify predetermined values for various criteria such as the near-price margin, near-price margin, and severity threshold values which may be utilized in the manner described in further detail below.

It is initially important to recognize that the alternative product that is recommended in the generated product summary by the product summary generator 10 may be a different brand, i.e. a product from a different manufacturer. However, it should also be appreciated that the alternative product recommended may be a different model of the same brand (i.e. from the same manufacturer) that is superior to the product reviewed in the generated product summary.

The alternative product that is selected from the plurality of candidate alternative products stored in the alternative products database 64 by the alternative products selection module 60 may share the same primary scenario, cost about the same, and be similar with respect to the key feature of the product category as the product reviewed in the generated product summary. However, as noted above, the selected alternative product recommended may be superior to the selected product being considered by the consumer and reviewed by the generated product summary, such recommendations regarding superior products being more likely to be followed by the consumer. Thus, the alternative product recommendation theme can be used to up-sell and encourage the consumer to purchase a more expensive product.

The alternative product identified and/or selected by the alternative product selection module 60 from the plurality of candidate alternative products of the alternative products database 64 may be attained based on the primary scenario in which a product scores the highest in its value rating, or a usage scenario that has been indicated as being the primary scenario. Thus, for the example of digital cameras, the alternative product selection module 60 may be implemented to only recommend alternative products that are suitable for sports-action photography, for instance, if this was the primary scenario for the digital camera being reviewed in the generated product summary.

In other, implementations, other criteria may be set that the recommended alternative product must satisfy. For instance, again for digital cameras, the alternative product selection module 60 may require the alternative product recommended in the generated product summary to have a comparable resolution that falls within the near-rank margin, and have a price that falls within the near-price margin. Thus, the alternative product selected by the alternative product selection module 60 from the plurality of candidate alternative products may be a product which is substantially the same, or better, with respect to near-rank margin, or near-price margin, severity values for a particular attribute. Of course, the above noted criteria are merely provided as examples and any appropriate criteria, including combinations of those noted above, may be used by the product summary generator 10 to determine which alternative product from the plurality of candidate alternative products should be recommended to the consumer.

The selection of the alternative product to be recommended may be implemented in a number of ways. In the present embodiment, the alternative product selection module 60 may be implemented to calculate severity differentials between each of the plurality of attributes for the plurality of candidate alternative products and the consumer selected product about which the narrative product summary is generated. This calculated severity differentials, severity values, near-price margin, near-rank margin, as well as the scenario of the selected product can be used by the alternative product selection module 60 of the product summary generator 10 to identify and select an alternative product form the plurality of alternative products for recommendation. Ten example cases are described in detail below that can be utilized to identify appropriate candidate alternative products that may be selected for use in providing the alternative product recommendation. Of course, it should be kept in mind that these cases are examples only and the present invention may be implemented in a different manner utilizing different criteria, and the like.

In the first case, the alternative product selection module 60 may select the alternative product for recommendation in the product summary narrative by identifying a candidate alternative product in the alternative products database 64 which provides a similar price, but better features than the selected product for which the narrative product summary is generated. In this regard, the alternative product selection module 60 may select a candidate alternative product which is within a predetermined near-price margin of the selected product, has at least one attribute with a severity value that is above a predetermined severity differential threshold, and has the same primary scenario as the selected product.

In the second case, the alternative product selection module 60 may select a candidate alternative product which has similar features, but a lower price than the selected product for which the narrative product summary is generated. This may be attained by selecting a candidate alternative product which is within a predetermined near-rank margin for the key attribute and for all attributes of the selected product that are above a predetermined high severity threshold, is priced lower and outside a predetermined near-price margin, and has the same primary scenario as the selected product.

In the third case, the alternative product selection module 60 may select a candidate alternative product which has better features, but a lower price than the selected product for which the narrative product summary is generated. This may be attained by selecting a candidate alternative product which has at least one attribute with a severity value that is above a predetermined severity differential threshold, is priced lower and outside a predetermined near-price margin, and has the same primary scenario as the selected product.

In the fourth case, the alternative product selection module 60 may select a candidate alternative product which has similar features, and a similar price as the selected product for which the narrative product summary is generated. This may be attained by selecting a candidate alternative product which is within a predetermined near-price margin, is within a predetermined near-rank margin for the key attribute and for all attributes of the selected product that are above a predetermined high severity threshold, and has the same primary scenario as the selected product.

In the fifth case, the alternative product selection module 60 may select a candidate alternative product which has similar features, and a similar price as the selected product for which the narrative product summary is generated, but is identified as being intended for a different usage scenario. This may be attained by selecting a candidate alternative product which is within a predetermined near-price margin, is within a predetermined near-rank margin for the key attribute and for all attributes of the selected product that are above a predetermined high severity threshold, and has a different primary scenario than the selected product.

As previously noted, the alternative product recommended may be a different model from the same manufacturer that is superior to the product reviewed in the generated product summary. In this regard, in the sixth case, the alternative product selection module 60 may select a candidate alternative product to provide a comparable up-sell product which has the same usage scenario and is the same brand as the selected product for which the narrative product summary is generated, except having better features and a higher price. This may be attained by selecting a candidate alternative product which is priced higher and outside a predetermined near-price margin; has at least one attribute with a severity value that is above a predetermined severity differential threshold, is from the same manufacturer as the selected product, and has the same primary scenario as the selected product.

Alternatively, in the seventh case, the alternative product selection module 60 may select a candidate alternative product to provide a comparable up-sell product which is the same brand as the selected product for which the narrative product summary is generated, except having better features, a higher price, and a different usage scenario. This may be attained by selecting a candidate alternative product which is priced higher and outside a predetermined near-price margin, has at least one attribute with a severity value that is above a predetermined severity differential threshold, is from the same manufacturer as the selected product, and has the same primary scenario as the selected product.

In the eighth case, the alternative product selection module 60 may select a candidate alternative product to cross-upsell in which a product from a different manufacturer that is greater in price and features than the selected product, and preferably within a predetermined near-rank margin, is recommended. In this embodiment, the alternative product may be selected either from within the same usage scenario, or from a different usage scenario. This case of cross-upsell is useful when the premier brand's products, from which an alternative product recommendation is desired, does not offer any product that is really better than the selected product for which the narrative product summary is generated. Thus, if no products of the premier brand provide superior features for the price, lower price for the features, or similar price and features, an alternative product from the premier brand can be identified and selected for recommendation, even though the alternative product may cost more.

In still the ninth case, the alternative product selection module 60 may select a candidate alternative product which is a “budget alternative” product from the same manufacturer by recommending a “downsell” product with a lower price and likely a bit less features, but preferably within a predetermined near-rank margin. In such a case, the candidate alternative product may be selected from within the same scenario or from a different scenario. This case is valuable when the selected product for which the narrative product summary is generated happens to be one of the most expensive, most fully featured products that the manufacturer offers so that there are no other product that can be recommended as better so as to allow upselling. Thus, in the described case, the message to the end user in the alternative product recommendation theme may be “if the model you are looking at is a bit too expensive for your budget, please consider this other model from the same brand, but which costs less, and requires you to merely trade off a couple of features to get the lower price.”

In a tenth case, the alternative product selection module 60 may select a candidate alternative product if the attributes or assertion models of a selected product have changed. For example, as shown schematically in the flow diagram 700 in FIG. 7, the system evaluates an existing product summary for the selected product in step 702. In step 710, the system reconciles the existing product summary into existing attributes associated with the selected product. The existing attributes include existing attribute names, existing attribute values, or existing attribute units.

In step 714, the summary generation module 50 resolves defined forms in the existing narrative product summary to existing assertion models.

In step 718, an engine 18 compares the existing attribute names, the existing attribute values, the existing attribute units, and the existing assertion models, respectively, to current component attribute names, current attribute values, current attribute units, and current assertion models to determine, in step 722, if at least one of the comparisons shows a change in the attribute name, the attribute value, the attribute unit, or the assertion model.

If, in step 726 the system determines that there was a change in a component of the attribute, then in step 730, the product attribute module 20 determines the new attributes associated with the selected product. The new attributes include at least changed attribute names, changed attribute values, or changed attribute units.

Once the new attributes are obtained, in step 732 the alternative product selection module 60 selects an alternative product based upon the factors discusses above.

In step 734, assertion model module 40 retrieves new assertion models that define forms in which assertions describe the selected product in a natural manner and which describe the selected alternative product in a natural manner.

After the new assertion models that define forms in which assertions describe the selected product and the selected alternative product are selected, in step 738 the summary generation module 50 combines the new attributes with the new retrieved assertion models to describe the selected product in the narrative and to combine the selected alternative product with another retrieved assertion model to recommend the selected alternative product in the narrative.

The recommendation made by the summary generation module 50 may be an advertisement for which compensation is received in exchange for recommending the selected alternative product. Also, the amount of compensation may be based on the frequency of the recommendation or the number of recommended products sold that is attributable to the recommendation. The alternative products may be stored as candidate alternative products in an alternative products database 64 from which they are selected by the alternative product selection module 60.

It should be emphasized that the above described cases are just example embodiments, and any of the embodiments can be modified or otherwise altered in other implementations based on the various considerations associated with implementing the product summary generator 10. For example, rather than identifying and selecting for an upsell beyond the near-price margin, the system may be implemented to identify and select an upsell alternative product which is higher in price by a certain amount than the product for which the product summary is generated.

Of course, more than one of the candidate alternative products may satisfy each of the desired criteria that are set forth in each of the various cases described above. In such instances, the candidate alternative products which best satisfy the criteria of each of the cases can be identified, and the alternative product select module 60 can then select one of the identified candidate alternative products for recommendation. In this regard, determining which of the candidate alternative products best satisfy the criteria may be facilitated by optionally weighting or biasing the determination to favor one candidate alternative product over the others.

For example, selection of a candidate alternative product may be selected by favoring one type of alternative product recommendation over another. This may be based on user behavior analysis which shows one type of alternative product recommendation is more effective in encouraging the user to follow the generated recommendation, for all users generally, or for users within a certain category. Alternatively, this may be based on analysis for a certain user who is making a repeat visit to the website, for example, and who has shown a predilection toward one type of alternative product recommendation in the past, e.g. shown to opt for a lower price for the same features, rather than for a chance to get extra features at the same price.

In another example, selection of a candidate alternative product may be facilitated by favoring one manufacturer over another, if more than one manufacturer's products are represented and are available for selection and recommendation in the alternative product recommendation theme. This may be based on any business reason, whether the reason is revenue driven or other strategic consideration.

In still another example, selection of a candidate alternative product may be facilitated based on text of the possible alternative product recommendation. In particular, the administrator of the product summary generator 10 or a website serviced thereby, may prefer lengthier texts over shorter ones, or prefer alternative product recommendations that point to a particular scenario of product with the belief that the particular scenario is currently “in vogue”. For instance, if smart-phones or camera-bearing devices are popular and are considered to be “the hottest thing” in handheld PDAs, the administrator may desire to bias the alternative product recommendations to direct the end user to candidate alternative PDA products that have these particular features.

In yet another example, selection of a candidate alternative product may be facilitated by favoring variation, either among the alternative product recommendation types, scenarios, and/or brands, so that if more than one alternative product recommendation is displayed, the selected alternative product that is recommended is not the same brand or type. Any of the above described biases, weightings or other parameters may be used to aid in selection of the candidate alternative product. However, such biases and weightings should not be overly emphasized in the preferred embodiment so as to neutralize the natural and logical assessment within the alternative product selection module 60 that identifies and recommends the best, most favorably comparing alternative product.

Preferably, the alternative product selection module 60 considers the alternative products identified in all of the above described cases before selecting the desired alternative product or products for recommendation in the product summary. In this regard, any appropriate criterion can be utilized in selection of the identified candidate products from the plurality of identified candidate alternative products satisfying the criterion of the cases set forth and described above. For example, in one embodiment, the brand of the identified candidate alternative product may be used to determine which of the products identified in each of the above cases is to be selected for use in recommendation in the product summary. In particular, the information that is stored in the alternative products database 64 may include information regarding the brand/manufacturer of each of the candidate alternative product identified. One or more of the brands of the candidate alternative products may be flagged as being a premier brand for a particular product category. The alternative product selection module 60 may then select the alternative product identified that is from a premier brand for use in the recommendation theme. Of course, use of brands is only one example of how the alternative product selection module 60 can select the desired alternative product from the identified candidate products, and in other embodiments, other criteria or methodologies may be used.

Moreover, each of the above-described cases preferably has corresponding assertions (with models, templates and snippets) that are utilized by the assertion model module 40. Thus, depending on which of the identified candidate alternative products is actually selected by the alternative product selection module 60, the summary generation module 50 utilizes corresponding assertions from the assertion model module 40 to provide a natural narrative that recommends the selected alternative product in the “Alternative Product Recommendation” theme of the generated product summary.

All of the foregoing considerations allow assertions for alternative product summaries to be modeled globally, i.e. without respect to which category of product is in question (e.g. vacuums, digital cameras or cars), so long as a basic vocabulary is resident in the product summary generator 10 by which to refer to the products in the particular product category. Generating an alternative product recommendation with an assertion for a camera that states “You can get a higher resolution for the same price in this Canon model”, or for a car that states “You can get additional horsepower for a similar price in this Mustang”, are essentially the same assertion template being instantiated for two different categories. However, some specialized explanation assertions may be implemented to further enhance the value prospect of the recommended alternative product of a particular product category and the recommendation itself.

For example, the benefit of more horsepower in a car is quite different from the benefit of more resolution in a digital camera, despite the fact that both generally follow a “more is better” pattern. In the case of the car, an assertion template may be provided with a condition requiring that the template be triggered only where the comparative attribute of the preceding sentence is “horsepower”. Following such a sentence, the next template may be implemented as follows:

The   additional   <AddedHorsepower::unit>  of   the <CompProduct::manufacturer> <CompProduct:ModelName> will put you in a different class of vehicles, enabling you to claim that you have a real “muscle car.”

The instantiated sentence using the above template may thus, read:

-   -   The additional 45 horses of the Ford Mustang RS will put you in         a different class of vehicles, enabling you to claim that you         have a real “muscle car.”

This type of follow-on sentence allows for domain-specific customization of the global structure of alternative product recommendations to a specific product category, and even to specific attribute-based benefits within the product category.

The APPENDIX attached hereto shows a small sampling of various naturally reading alternative product recommendations that have been generated using a product summary generator in accordance with one example implementation of the present invention. In particular, the rows of the APPENDIX list various models of handheld PCs and PDAs while the columns set forth possible alternative product recommendations for the corresponding models of handheld PCs or PDAs. Each of the possible alternative product recommendations is a result of the alternative product selection module 60 identifying a possible candidate product by applying the various example cases described in detail above. Thus, if the selected product for which the product summary is generated is HP Ipaq™ Pocket PC H3635 (the first handheld PC listed in the APPENDIX), various alternative product recommendations may be generated by the product summary generator that recommends, for example, Palm Tungsten™ or Sony CLIE™ models. As shown, each potential alternative product recommendation briefly describes how these alternative products compare to the selected product. One or more of the possible alternative product recommendations may be ultimately selected for display in the “Alternative Product Recommendation” theme of the generated product summary. Of course, it should be understood that the alternative product recommendations set forth in the attached APPENDIX are provided as illustrative examples only, and the present invention may be applied in any other appropriate manner.

In accordance with another aspect of the present invention, the “Alternative Product Recommendation” theme discussed in detail above that is generated by the product summary generator 10 may actually be utilized as an advertisement in which the manufacturers of products pay fees or provide other compensation to the administrator of the product summary generator 10 for recommending their products. Thus, the described alternative product recommendation theme may be used to cross-sell a product where the alternative product recommended is a product from a different manufacturer than the selected product described in the product summary. Alternatively, the described alternative product recommendation theme may be used to up-sell a product where the alternative product recommended is a different, and more expensive, model from the same manufacturer.

In the above regard, only products from manufacturers that pay the advertisement fee may be stored in the alternative products database 64 for consideration for use in the alternative product recommendation theme of the generated product summary. In addition, the amount of the advertisement fees itself, may be used by the alternative product selection module 60 in the selection of one of the identified candidate alternative products for recommendation in the product summary. In particular, the amount of advertisement fee may be correlated to the frequency in which a particular alternative product is to be recommended in the Alternative Product Recommendation theme. Of course, other parameters may be utilized to control the frequency in which a particular alternative product is recommended. In other implementations, the fees can be paid upon actual sales of the recommended product that is attributed to the product being recommended in the Alternative Product Recommendation theme, the attribution of the actual sales to the recommendation being implemented in any appropriate manner.

To the extent that competing product manufactures vie for having their own products recommended in the Alternative Product Recommendation theme, any appropriate method may be utilized to determine which product is actually recommended. In this regard, the competing product manufacturers may be allowed to bid against each other, like in an auction, for the right to be recommended in the Alternative Product Recommendation, the right to the recommendation being awarded to the highest bidding manufacturer.

In the above regard, in another embodiment, the manufacturer may bid to be the only manufacturer, or one of a few manufacturers, whose products are included in a limited sub-pool of alternative products which are eligible for selection for recommendation by the alternative product selection module 60. The best and most favorable alternative product in the sub-pool would then be selected for recommendation. If more than one manufacturer is represented with products in the limited sub-pool for the same category of products, an optional weighting factor based on the highest bid may be provided so that the products from the highest bidder may be selected. Of course, in such an implementation, the selection of the alternative product may be based on another business consideration, for example, if a revenue model other than bidding is utilized for payment.

Whereas various themes have been described in detail above, including the “Alternative Product Recommendation” theme, the editors that set up the product summary generator 10 may add any number of supplemental themes as well. For example, in the product category of digital cameras, editors may add “suggested uses” theme or “suggested accessories” theme, and so forth. Such supplemental themes would preferably be based on secondary attributes and implemented in a manner similar to that described above relative to the other themes. For example, suggested accessories may include a dedicated photo printer, but for low-resolution cameras, this would not be a recommended accessory since such cameras do not have sufficient resolution to make such printers worthwhile.

The compilation of assertions and their modification via merging and insertion of transitional phrases can leave the resulting “sentences” in an interim state where they lack correct punctuation and capitalization, etc. Routines may be called and utilized by the product summary generator 10 near the end of every generation sequence to clean up such formatting. Such routines are already known in the art and need not be described in further detail here. When each theme has been generated, the themes desired which may be predetermined or otherwise selected from the interface to the product summary generator 10 may be called up, labeled, and generated as a list of paragraphs.

Based on the interface of the product summary generator 10, the product summary that has been generated in the manner described above may be displayed and/or printed, etc. In one embodiment, the generated product summary may be a continuous body of text. In another embodiment, the themes of the generated product summary may be set forth in separate paragraphs as shown in the example product summary 120 of FIG. 2B. Alternatively, the interface of the product summary generator 10 may include a set of tabs, each tab labeled with one theme of the product summary. In still another alternative interface, a list of the themes may be provided with a cursor-over effect such that whichever label the person holds the cursor over, the appropriate theme is displayed in a shared display panel. Of course, the above noted interface designs are exemplary only and many alternatives may be implemented instead.

It should now be apparent how the present invention provides a product summary generator which generates product summaries that sound and read naturally so that it can be easily understood by the reader and information presented therein can be used to aid in purchasing decisions. In particular, it should now be evident how the product summary generator such as that shown in FIG. 1 may be utilized to automatically generate product summaries having a form such as that schematically shown in FIG. 2A from various inputs such as attribute values, attribute ranks, prices, scenario information, product category information, attribute importance rating, secondary attribute functions and rules, and assertion models. By providing the required information regarding a particular product of a product category (via a file or a database), the product summary generator can be used to automatically generate a product summary which reads naturally.

Furthermore, it should also be evident how the present invention provides a novel method of generating product summaries, an example method being schematically illustrated in the flow diagram 200 of FIG. 4. As shown, the method as shown in flow diagram 200 includes step 202 in which attribute(s) associated with the selected product is determined. In step 204, secondary attributes are determined, and assertion model having assertion templates is retrieved in step 206. Severity values are then determined in step 208 based on importance ratings and attribute ranks. In accordance with the illustrated method, a naturally reading narrative regarding the selected product is generated in step 210 by combining a second attribute with retrieved assertion model. In one embodiment, the generated product summary of step 210 would include an alternative product recommendation in which an alternative product is identified and a brief description thereof is provided for consideration by the consumer. The secondary attribute used may be based on the determined severity values of step 208.

Moreover, it should also be evident how the present invention provides still another novel method of generating product summaries such as that schematically illustrated in the flow diagram 300 of FIG. 5. As shown, the method as shown in flow diagram 300 includes step 302 in which at least one attribute associated with the selected product is determined. As described, the attribute includes at least one of an attribute name, an attribute value, and an attribute unit. The method also includes step 306 in which an alternative product is identified. In step 308, assertion models are retrieved to describe the selected product, and to recommend an alternative product in a natural manner. Finally, the method includes step 310 in which a naturally reading narrative is generated by combining the attribute and the selected alternative product with the retrieved assertion models such that the generated narrative includes a recommendation of the selected alternative product.

Of course, it should also be noted that the above described flow diagrams are merely example methods of generating product summaries and the present invention should not be construed to be limited thereto. Various other methods for generating product summaries should be now evident in view of the teachings set forth above. Moreover, various other steps as described previously relative to the product summary generator 10 may be provided as well in other embodiments of the method. For example, the steps of assigning key attributes, calculating an average price of comparable products, and/or designating themes to the assertion models, etc. as described above relative to the product summary generator 10 may be provided as well.

While various embodiments in accordance with the present invention have been shown and described, it is understood that the invention is not limited thereto. The present invention may be changed, modified and further applied by those skilled in the art. Therefore, this invention is not limited to the detail shown and described previously, but also includes all such changes and modifications.

APPENDIX Product Name AdBrief Themes -> HP iPaq [CompBetterFeatures [CompCrossUpSell] [AltSameFeatures [CompCrossUpSell] Pocket PC ForLess] [0.81] To [0.39] For a bit more AndPrice] [0.36] If [0.27] For a H3650 help decide if the HP money than the HP you would like to little more money iPAQ H3650 is your iPAQ H3650, you can consider a than the HP iPAQ best choice in a get the Palm wireless enabled H3650, you could handheld, you may Tungsten T2 having handheld with buy the Sony want to take a look at its richer display (16- many similar CLIÉ PEG- the Sony CLIÉ PEG- bit, 64,000-color, as features to the HP N610C having its SJ22, as it will provide opposed to 12-bit, iPAQ H3650, and richer display you some advantages 4,000-color). If you for around the (16-bit, 64,000- at a lower price than want to look at an same cost, then color, as opposed the HP iPAQ H3650. interesting upgrade check out the to 12-bit, 4,000- For example, it has a option, you could pay Sony CLIE PEG- color). As an 16-bit, 64,000-color just a bit more and TJ37. It sells for interesting display, which is richer have the Palm around $275. Just upgrade option, than the HP iPAQ Tungsten T2, which like the HP model, you could pay H3650 (12-bit, 4,000- has a few additional the Sony has 32 MB just a bit more color). Moreover, the benefits. For memory. And and have the Sony CLIÉ PEG-SJ22 example, it has a 16- it has a 320 × 320 Sony CLIÉ PEG- usually goes for bit, 64,000-color screen resolution, N610C, which around $150, as display, compared to which you can has a few opposed to $255 for the 12-bit, 4,000- compare to the additional the HP iPAQ H3650. color of the HP iPAQ 240 × 320 of the HP benefits. For H3650. And it will model. The higher example, it has a only cost you about resolution will 16-bit, 64,000- $40 more than the come in handy if color display, HP iPAQ H3650 (i.e., you want to use compared to the around $295). your handheld as 12-bit, 4,000- a viewer for color of the HP photos from a iPAQ H3650. And digital camera. it will only cost you about $35 more than the HP iPAQ H3650 (i.e., around $290). HP iPaq [AltSameFeaturesAnd [AltSameFeaturesAnd [AltCrossUpSell] [AltCrossUpSell] BlackBerry Price] [0.81] If you are Price] [0.54] The [0.37] If you' re [0.23] Before you W1000 interested in a Sony CLIE PEG- interested in decide to buy the multimedia handheld TJ27 provides some something that is HP iPAQ with many similar similar features to the a little more Blackberry features to the HP HP iPAQ Blackberry feature-rich, you W1000, you iPAQ Blackberry W1000, in a could get the Palm might want to W1000, and for multimedia handheld. Zire 71, a compare it to the around the same cost, It goes for around multimedia Sony CLIE PEG- then check out the $190. Similar in many handheld, which TJ37, a wireless Palm Tungsten E. It of its features, the has certain enabled sells for around $190. Sony weighs just 5.1 advantages. For handheld which Similar in many of its ounces, which is example, it has a has a few features, the Palm similar to the 5 320 × 320 screen additional weighs just 4.6 ounces of the HP resolution, benefits. ounces, which is model, and it also compared to the Although it will similar to the 5 ounces has a mere 0.5 inch 132 × 65 of the HP cost you about of the HP model, and thickness, which iPAQ Blackberry $75 more than it also has a mere 0.5 again is similar to the W1000. The the HP iPAQ inch thickness, which 0.9 inch of the HP higher resolution Blackberry again is similar to the model. The weight of will come in handy W1000, it will 0.9 inch of the HP a handheld is if you want to use bring you into a model. The weight of a significant for those your handheld as different class of handheld is significant who will be holding it a viewer for handhelds. For for those who will be for lengthy periods of photos from a example, it has holding it for lengthy time and may be digital camera. 32 MB memory, periods of time and subject to hand and Additionally, the compared to the may be subject to finger fatigue. Palm Zire 71 has 512 KB of the HP hand and finger 16 MB memory, iPAQ Blackberry fatigue. as opposed to 512 KB W1000. for the HP Additionally, the iPAQ Blackberry Sony CLIE PEG- W1000. And at TJ37 has a $225, it costs just 320 × 320 screen $25 more than the resolution, as HP iPAQ opposed to Blackberry 132 × 65 for the W1000. HP iPAQ Blackberry W1000. The higher resolution will come in handy if you want to use your handheld as a viewer for photos from a digital camera. Palm Zire [CompUpSell] [0.35] If [AltUpSell] [0.26] you want to look at an Before you decide to interesting upgrade buy the Palm Zire, option, you could pay you might want to just a bit more and get compare it to the the Palm Tungsten T, Palm Tungsten E, a which has a few multimedia handheld additional benefits. which has certain Although it will cost advantages. Although you about $225 more it will cost you about than the Palm Zire, it $120 more than the will bring you into a Palm Zire, it will bring different class of you into a different handhelds. For class of handhelds. instance, it has a For example, it has 320 × 320 screen 32 MB memory, resolution, compared compared to the 2 MB to the 160 × 160 of the of the Palm Zire. Palm Zire. The higher Additionally, the Palm resolution will come in Tungsten E has a handy if you want to 320 × 320 screen use your handheld as resolution, as a viewer for photos opposed to 160 × 160 from a digital camera. for the Palm Zire. The Additionally, the Palm higher resolution will Tungsten T has 16 MB come in handy. if you memory, as opposed want to use your to 2 MB memory for handheld as a viewer the Palm Zire. for photos from a digital camera. Sony CLIÉ [CompDownSell] [AltDownSell] [0.15] PEG-NX70V [0.36] If you want to Without spending as spend a little less much money, you money, you could get could get the Sony the Sony CLIÉ PEG- CLIÉ PEG-N610C. It N760C. It has 8 MB has 8 MB memory, memory, which you which you can can compare to the 16 MB compare to the 16 MB of the Sony CLIÉ of the Sony CLIÉ PEG-NX70V. And it PEG-NX70V. Also, its will cost you about $85 $290 street price is less than the Sony about $165 less than CLIÉ PEG-NX70V the Sony CLIÉ PEG- (i.e., around $370). NX70V. Palm [CompUpSell] [0.46] [AltUpSell] [0.36] If Tungsten T As an interesting you' re interested in Handheld upgrade option, you something that is a could pay just a bit little more feature- more and have the rich, you could get Palm Tungsten T3, the Palm Tungsten C, which has certain a wireless enabled advantages. Although handheld, which has it will cost you about a few additional $105 more than the benefits. For Palm Tungsten T, it instance, it has 64 MB will bring you into a memory, different class of compared to the 16 MB handhelds. For of the Palm example, it has 64 MB Tungsten T. And at memory, compared to $355, it costs just $60 the 16 MB of the Palm more than the Palm Tungsten T. Tungsten T. HP iPaq [CompBetterFeatures [CompBetterFeatures [AltSameFeatures [AltSameFeatures Pocket PC ForLess] [0.84] To ForPrice] [0.57] For AndPrice] [0.33] If AndPrice] [0.31] H3635 help decide if the HP around the same you would like to The Sony CLIÉ iPAQ H3635 is your price as the HP iPAQ consider a PEG-TJ37 best choice in a H3635, you could get multimedia provides some handheld, you may the Palm Tungsten handheld with similar features to want to take a look at T2 with its richer many similar the HP iPAQ the Sony CLIÉ PEG- display (16-bit, features to the HP H3635, in a SJ22, as it will give 64,000-color, as iPAQ H3635, and wireless enabled you certain additional opposed to 12-bit, for around the handheld. It costs benefits at a lower 4,000-color). same cost, then around $275. price than the HP check out the Just like the HP iPAQ H3635. For Palm Tungsten model, the Sony example, it has a 16- T2. It sells for has 32 MB bit, 64,000-color around $295. Just memory. And it display, which is richer like the HP model, has a 320 × 320 than the HP iPAQ the Palm has 32 MB screen resolution, H3635 (12-bit, 4,000- memory. And which you can color). Moreover, the it has a 320 × 320 compare to the Sony CLIÉ PEG-SJ22 screen resolution, 240 × 320 of the usually goes for about which you can HP model. The $150, compared to compare to the higher resolution $300 for the HP iPAQ 240 × 320 of the HP will come in H3635. model. The higher handy if you want resolution will to use your come in handy if handheld as a you want to use viewer for photos your handheld as from a digital a viewer for camera. photos from a digital camera. HP iPaq [CompBetterFeatures [AltSameFeaturesAnd [AltCrossUpSell] Pocket PC ForPrice] [0.57] Before Price] [0.37] The [0.35] Before you H3765 purchasing the HP Palm Tungsten C decide to buy the iPAQ H3765, you may provides some similar HP iPAQ H3765, want to consider the features to the HP you might want to Palm Tungsten T3, iPAQ H3765, in a compare it to the which for around the wireless enabled Sony CLIÉ PEG- same price has a 16- handheld. It costs UX50, a bit, 64,000-color around $410. In multimedia display (i.e., richer many ways like the handheld which than the 12-bit, 4,000- HP, the Palm has 64 MB has certain color of the HP iPAQ memory and advantages. H3765). weighs just 6.3 Although it will ounces. cost you about $130 more than the HP iPAQ H3765, it will bring you into a different class of handhelds. For example, it has a 16-bit, 64,000- color display, compared to the 12-bit, 4,000-color of the HP iPAQ H3765. Garmin iQue [CompBetterPriceForFeatures] [CompBetterPriceFor 3600 [0.9] It's Features] [0.62] It's possible for you to get possible for you to some similar features get some similar to the Garmin iQue features to the 3600, but at a lower Garmin iQue 3600, price of around $190, but at a lower price of in the Palm OS-based around $190, in the Palm Tungsten E. Just Palm OS-based Sony like the Garmin, the CLIE PEG-TJ27. Just Palm has 32 MB like the Garmin, the memory and has a 16- Sony has 32 MB bit, 64,000-color memory and has a display. 16-bit, 64,000-color display. Sony CLIÉ [CompUpSell] [0.56] [AltUpSell] [0.26] PEG-SJ33 For just a little more Before you decide to money, you could buy the Sony CLIÉ move up to the Sony PEG-SJ33, you might CLIE PEG-TJ35, want to compare it to which has a few the Sony CLIE PEG- additional benefits. For TJ37, a wireless instance, it has 32 MB enabled handheld memory, against the which has a few 16 MB of the Sony additional benefits. CLIÉ PEG-SJ33. And Although it will cost it will only cost you you about $105 more about $20 more than than the Sony CLIÉ the Sony CLIÉ PEG- PEG-SJ33, it will SJ33 (i.e., around bring you into a $190). different class of handhelds. For instance, it has 32 MB memory, against the 16 MB of the Sony CLIÉ PEG- SJ33. Handspring [CompUpSell] [0.32] If [AltUpSell] [0.24] Visor you want to look at an Before you decide to Platinum interesting upgrade buy the Palm (Silver) option, you could pay Handspring Visor just a bit more and Platinum, you might have the Palm m130, want to compare it to which has a few the Palm Tungsten E, additional benefits. a multimedia Although it will cost handheld which has you about $85 more certain advantages. than the Palm Although it will cost Handspring Visor you about $125 more Platinum, it will bring than the Palm you into a different Handspring Visor class of handhelds. Platinum, it will bring For example, it has a you into a different 16-bit, 64,000-color class of handhelds. display, against the 4- For example, it has a bit, 16 gray level of the 16-bit, 64,000-color Palm Handspring display, against the Visor Platinum. 4-bit, 16 gray level of the Palm Handspring Visor Platinum. In addition, the Palm Tungsten E has 32 MB memory, as opposed to 8 MB for the Palm Handspring Visor Platinum. Palm Zire 71 [CompUpSell] [0.49] If [AltUpSell] [0.33] you want to look at an Before you decide to interesting upgrade buy the Palm Zire 71, option, you could pay you might want to just a bit more and get compare it to the the Palm Tungsten T2, Palm Tungsten T2, a which has certain consumer-oriented advantages. Although handheld which has a it will cost you about few additional $70 more than the benefits. Although it Palm Zire 71, it will will cost you about bring you into a $70 more than the different class of Palm Zire 71, it will handhelds. For bring you into a instance, it has 32 MB different class of memory, against the handhelds. For 16 MB of the Palm example, it has 32 MB Zire 71. memory, against the 16 MB of the Palm Zire 71. Palm [AltDownSell] [0.5] If Tungsten C you want to spend a little less money, you could get many similar advantages in the Palm m515. It has 16 MB memory, which you can compare to the 64 MB of the Palm Tungsten C. It also has a 160 × 160 screen resolution, compated to 320 × 320. The higher resolution will come in handy if you want to use your handheld as a viewer for photos from a digital camera. Additionally, the Palm m515 has a 160 × 160 screen resolution, as opposed to 320 × 320 for the Palm Tungsten C.. The higher resolution will come in handy if you want to use your handheld as a viewer for photos from a digital camera. Also, its $315 street price is about $95 less than the Palm Tungsten C. Compaq [AltSameFeaturesAnd [CompBetterFeatures [CompBetterFeatures [CompCrossUpSell] iPaq H3630 Price] [0.67] If you ForPrice] [0.55] ForLess] [0.48] [0.31] For a would like to consider There's an interesting In contrast to this bit more money a multimedia handheld alternative to the HP HP model, you than the HP with many similar Compaq iPAQ can enjoy certain Compaq iPAQ features to the HP H3630. Selling at advantages (while H3630, you can Compaq iPAQ H3630, around $195, the getting a lower buy the Palm and for around the Sony CLIÉ PEG- price) in the Sony Tungsten T2 with same cost, then check SJ30 has a 16-bit, CLIÉ PEG-SJ22. its richer display out the Palm Tungsten 64,000-color display, First of all, it has a (16-bit, 64,000- E. It sells for around which is richer than 16-bit, 64,000- color, as opposed $190. Just like the HP that of the HP color display, to 12-bit, 4,000- model, the Palm has product (12-bit, which is richer color). As an 32 MB memory. And it 4,000-color). than the HP interesting has a 320 × 320 screen Compaq iPAQ upgrade option, resolution, which you H3630 (12-bit, you could pay can compare to the 4,000-color). just a bit more 240 × 320 of the HP Moreover, the and get the Palm model. The higher Sony CLIÉ PEG- Tungsten T2, resolution will come in SJ22 usually goes which has a few handy if you want to for about $150, additional use your handheld as compared to $195 benefits. a viewer for photos for the HP Although it will from a digital camera. Compaq iPAQ cost you about H3630. $100 more than the HP Compaq iPAQ H3630, it will bring you into a different class of handhelds. For example, it has a 16-bit, 64,000- color display, compared to the 12-bit, 4,000- color of the HP Compaq iPAQ H3630. AlphaSmart [CompBetterFeatures [AltSameFeaturesAnd [AltCrossUpSell] [AltSameFeatures Dana ForPrice] [0.91] Before Price] [0.47] If you [0.35] Before you AndPrice] [0.23] purchasing the are interested in a decide to buy the If you would like Alphasmart Dana, you wireless enabled Alphasmart Dana, to consider a might want to take a handheld with many you might want to consumer- look at the Palm similar features to the compare it to the oriented Tungsten T3, which Alphasmart Dana, Sony CLIÉ PEG- handheld with for around the same and for around the NX70V, a many similar price has 64 MB same cost, then multimedia features to the memory (i.e., larger check out the Sony handheld which Alphasmart than the 8 MB of the CLIE PEG-TH55. It has certain Dana, and for Alphasmart Dana). sells for around $380. advantages. For around the same The Palm model also example, it has a cost, then check has a mere 3 inch mere 2.8 inch out the Palm width, which is width, compared Tungsten T3. It narrower than that of to the 12.4 inch of sells for around the Alphasmart Dana the Alphasmart $400. (12.4 inch). The size Dana. The size of of the device can be a the device can be significant factor, for a significant factor, example, if you intend for example, if you to carry the device in intend to carry the your shirt pocket or device in your shirt pants pocket. pocket or pants pocket. In addition, the Sony CLIÉ PEG-NX70V weighs just 8 ounces, as opposed to 2 lbs for the Alphasmart Dana. The weight of a handheld is significant for those who will be holding it for lengthy periods of time and may be subject to hand and finger fatigue. Also, its $455 street price is only $75 more than the Alphasmart Dana. Sony CLIÉ [CompUpSell] [0.4] If [AltUpSell] [0.31] PEG-TG50 you want to look at an Before you decide to interesting upgrade buy the Sony CLIÉ option, you could pay PEG-TG50, you just a bit more and might want to have the Sony CLIÉ compare it to the PEG-NX80V, which Sony CLIE PEG- has certain TH55, a wireless advantages. Although enabled handheld it will cost you about which has certain $140 more than the advantages. Although Sony CLIÉ PEG- it will cost you about TG50, it will bring you $100 more than the into a different class of Sony CLIÉ PEG- handhelds. For TG50, it will bring you example, it has 32 MB into a different class memory, compared to of handhelds. For the 16 MB of the Sony example, it has 32 MB CLIÉ PEG-TG50. memory, compared to the 16 MB of the Sony CLIÉ PEG-TG50. Asus MyPal [CompBetterPriceForFeatures] [CompSameFeatures A716 [0.55] You AndPrice] [0.22] As could get some similar an additional features to the Asus alternative, you can Mypal A716, but at a get some similar lower price of around features to the Asus $355, in the Palm OS- Mypal A716, and at a based Palm Tungsten comparable price of C. around $410, in the Palm OS-based Palm Tungsten C. VISOR [CompUpSell] [0.38] If [AltUpSell] [0.28] EDGE BLUE you want to look at an Before you decide to 8 MB USB W/ interesting upgrade buy the Palm Visor STOWAWAY option, you could pay Edge Blue 8 MB, you PORTABLE just a bit more and might want to KEYBOARD- have the Palm m130, compare it to the KIT which has certain Palm Tungsten E, a advantages. Although multimedia handheld it will cost you about which has a few $70 more than the additional benefits. Palm Visor Edge Blue Although it will cost 8 MB, it will bring you you about $110 more into a different class of than the Palm Visor handhelds. For Edge Blue 8 MB, it will instance, it has a 16- bring you into a bit, 64,000-color different class of display, compared to handhelds. For the 4-bit, 16 gray level example, it has a 16- of the Palm Visor bit, 64,000-color Edge Blue 8 MB. display, compared to the 4-bit, 16 gray level of the Palm Visor Edge Blue 8 MB. In addition, the Palm Tungsten E has 32 MB memory, as opposed to 8 MB for the Palm Visor Edge Blue 8 MB. Palm [CompDownSell] [AltDownSell] [0.29] Tungsten T2 [0.39] If you want to Without spending as Handheld spend a little less much money, you money, you could get could get many the Palm Zire 71. It similar advantages in has 16 MB memory, the Palm Handspring which you can Visor Prism. It has 8 MB compare to the 32 MB memory, which of the Palm Tungsten you can compare to T2. And at $250, you'll the 32 MB of the pay $45 less than Palm Tungsten T2. It you would for the also has a 160 × 160 Palm Tungsten T2. screen resolution, compated to 320 × 320. The higher resolution will come in handy if you want to use your handheld as a viewer for photos from a digital camera. In addition, the Palm Handspring Visor Prism has a 160 × 160 screen resolution, as opposed to 320 × 320 for the Palm Tungsten T2. The higher resolution will come in handy if you want to use your handheld as a viewer for photos from a digital camera. Also, its $170 street price is about $125 less than the Palm Tungsten T2. Sony CLIÉ [CompDownSell] [AltDownSell] [0.15] If PEG-UX50 [0.39] If you want to you want to spend a spend a little less little less money, you money, you could get could get many many similar similar advantages in advantages in the the Sony CLIÉ PEG- Sony CLIÉ PEG- T615CS. It has 16 MB NR70. It has 16 MB memory, which memory, which you you can compare to can compare to the 40 MB the 40 MB of the of the Sony CLIÉ Sony CLIÉ PEG- PEG-UX50. And it will UX50. And at $320, it cost you about $90 costs about $235 less less than the Sony than the Sony CLIÉ CLIÉ PEG-UX50 (i.e., PEG-UX50. around $465). Sharp YO- [CompCrossUpSell] [AltCrossUpSell] [AltCrossUpSell] P20 [0.21] For a little more [0.07] Before you [0.06] Before you Personal money than the Sharp decide to buy the decide to buy the Organizer YO-P20, you could Sharp YO-P20, you Sharp YO-P20, buy the Palm might want to you might want to Tungsten T2 having its compare it to the compare it to the larger memory (32 MB, Palm Tungsten E, a Sony CLIE PEG- as opposed to multimedia handheld TJ27, a 768 KB). As an which has a few multimedia interesting upgrade additional benefits. handheld which option, you could pay Although it will cost has a few just a bit more and get you about $170 more additional benefits. the Palm Tungsten T2, than the Sharp YO- Although it will which has certain P20, it will bring you cost you about advantages. Although into a different class $170 more than it will cost you about of handhelds. For the Sharp YO- $275 more than the instance, it has 32 MB P20, it will bring Sharp YO-P20, it will memory, against you into a different bring you into a the 768 KB of the class of different class of Sharp YO-P20. handhelds. For handhelds. For example, it has 32 MB instance, it has 32 MB memory, memory, compared to compared to the the 768 KB of the 768 KB of the Sharp YO-P20. Sharp YO-P20. Toshiba [CompSameFeatures [AltSameFeaturesAnd [AltSameFeatures 2032SP AndPrice] [0.68] Note Price] [0.38] If you AndPrice] [0.15] that for a comparable are interested in a The Sony CLIE price of about $375, wireless enabled PEG-TH55 the Palm OS-based handheld with many provides some Palm Tungsten T3 similar features to the similar features to would give you similar Sprint Toshiba the Sprint Toshiba features to the Sprint 2032SP, and for 2032SP, in a Toshiba 2032SP. Just around the same wireless enabled like the Sprint model, cost, then check out handheld. It costs the Palm has a 16-bit, the Palm Tungsten C. around $380. Just 64,000-color display. It sells for around like the Sprint, the And note that it weighs $355. Just like the Sony has 32 MB just 5.5 ounces, which Sprint model, the memory and has a is more or less the Palm has a 16-bit, 16-bit, 64,000- same as the 7 ounces 64,000-color display. color display. of the Sprint model. And it weighs just 6.3 ounces, which you can compare to the 7 ounces of the Sprint model. The weight of a handheld is significant for those who will be holding it for lengthy periods of time and may be subject to hand and finger fatigue. Handspring [CompBetterFeatures [AltSameFeaturesAnd [AltSameFeatures [AltCrossUpSell] Treo 300 ForPrice] [0.82] Price] [0.59] If you AndPrice] [0.5] [0.26] If you' re There's a rather are interested in a The Sony CLIE interested in tempting alternative to wireless enabled PEG-TH55 offers something that is the Sprint Handspring handheld with many some similar a little more Treo 300. Selling at similar features to the features to the feature-rich, you around $375, the Palm Sprint Handspring Sprint Handspring could get the Tungsten T3 has 64 MB Treo 300, and for Treo 300, in a Sony CLIÉ PEG- memory, which is around the same wireless enabled NR70, a larger than that of the cost, then check out handheld. It has a multimedia Sprint product (16 MB). the Palm Tungsten C. street price of handheld, which The Palm model It sells for around around $380. has certain also has a 16-bit, $355. Similar in many Similar in many of advantages. For 64,000-color display, of its features, the its features, the example, it has a which is richer than Palm weighs just 6.3 Sony weighs just 16-bit, 64,000- that of the Sprint ounces, which is 6.5 ounces, which color display, Handspring Treo 300 similar to the 5.7 is similar to the 5.7 against the 12- (12-bit, 4,000-color). ounces of the Sprint ounces of the bit, 4,000-color of model, and it also Sprint model, and the Sprint has a mere 0.7 inch it also has a mere Handspring Treo thickness, which 0.6 inch thickness, 300. Also, its again is similar to the which again is $465 street price 0.8 inch of the Sprint similar to the 0.8 is only $75 more model. The weight of inch of the Sprint than the Sprint a handheld is model. The weight Handspring Treo significant for those of a handheld is 300. who will be holding it significant for for lengthy periods of those who will be time and may be holding it for subject to hand and lengthy periods of finger fatigue. time and may be subject to hand and finger fatigue. Casio [CompBetterPriceForFeatures] [CompBetterPriceFor [CompBetterFeatures Cassiopeia [0.94] For a Features] [0.55] You ForLess] [0.52] EM-500 lower price of around could get some In contrast to this (Blue) $135, the Palm OS- similar features to the Casio model, you based Sony CLIÉ Casio Cassiopeia can enjoy certain PEG-SJ20 would give EM-500, but at a advantages (while you similar features to lower price of around getting a lower the Casio Cassiopeia $295, in the Palm price) in the Palm EM-500. Just like the OS-based Palm Tungsten T3. To Casio model, the Sony Tungsten T. Just like begin with, it has has 16 MB memory. the Casio, the Palm 64 MB memory, And note that it has a has 16 MB memory which is larger 320 × 320 screen and has a 16-bit, than the Casio resolution, which is 64,000-color display. Cassiopeia EM- fairly comparable to 500 (16 MB). the 240 × 320 of the Moreover, the Casio model. Palm Tungsten T3 typically costs around $400, as opposed to $500 for the Casio Cassiopeia EM- 500. Casio [CompBetterPriceForFeatures] [CompBetterFeatures [CompBetterPrice Cassiopeia [0.94] For a ForLess] [0.57] To ForFeatures] EM-500 lower price of around help decide if the [0.55] You could (Green) $135, the Palm OS- Casio Cassiopeia get some similar based Sony CLIÉ EM-500 is your best features to the PEG-SJ20 would give choice in a handheld, Casio Cassiopeia you similar features to you might want to EM-500, but at a the Casio Cassiopeia take a look at the lower price of EM-500. Just like the Palm Tungsten T3, around $295, in Casio model, the Sony which will provide you the Palm OS- has 16 MB memory. certain additional based Palm And note that it has a benefits at a lower Tungsten T. Just 320 × 320 screen price than the Casio like the Casio, the resolution, which is Cassiopeia EM-500. Palm has 16 MB fairly comparable to For example, it has memory and has a the 240 × 320 of the 64 MB memory, 16-bit, 64,000- Casio model. which is larger than color display. the Casio Cassiopeia EM-500 (16 MB). Furthermore, the Palm Tungsten T3 usually goes for about $400, compared to $500 for the Casio Cassiopeia EM-500. Casio [CompBetterPriceForFeatures] [CompBetterFeatures [CompBetterPrice Cassiopeia [0.96] It's ForLess] [0.57] To ForFeatures] EM-500 possible for you to get help decide if the [0.54] For a lower (Red) some similar features Casio Cassiopeia price of around to the Casio EM-500 is your best $295, the Palm Cassiopeia EM-500, choice in a handheld, OS-based Palm but at a lower price of you may want to take Tungsten T would around $135, in the a look at the Palm give you similar Palm OS-based Sony Tungsten T3, which features to the CLIÉ PEG-SJ20. Just will give you certain Casio Cassiopeia like the Casio model, additional benefits at EM-500. Just like the Sony has 16 MB a lower price than the the Casio, the memory. And note that Casio Cassiopeia Palm has 16 MB it has a 320 × 320 EM-500. For memory and has a screen resolution, example, it has 64 MB 16-bit, 64,000- which is fairly memory, which is color display. comparable to the larger than the Casio 240 × 320 of the Casio Cassiopeia EM-500 model. (16 MB). Moreover, the Palm Tungsten T3 usually goes for about $400, compared to $500 for the Casio Cassiopeia EM-500. Casio [CompBetterPriceForFeatures] [CompBetterPriceFor [CompBetterFeatures Cassiopeia [0.94] For a Features] [0.54] For a ForLess] [0.51] EM-500 lower price of around lower price of around In contrast to this (Sky Blue) $135, the Palm OS- $295, the Palm OS- Casio model, you based Sony CLIÉ based Palm can gain certain PEG-SJ20 would give Tungsten T would advantages (while you similar features to give you similar getting a lower the Casio Cassiopeia features to the Casio price) in the Palm EM-500. Just like the Cassiopeia EM-500. Tungsten T3. For Casio model, the Sony Just like the Casio, starters, it has 64 MB has 16 MB memory. the Palm has 16 MB memory, And note that it has a memory and has a which is larger 320 × 320 screen 16-bit, 64,000-color than the Casio resolution, which is display. Cassiopeia EM- fairly comparable to 500 (16 MB). the 240 × 320 of the Moreover, the Casio model. Palm Tungsten T3 typically costs around $400, compared to $500 for the Casio Cassiopeia EM- 500. Casio [CompBetterPriceForFeatures] [CompBetterPriceFor [CompBetterFeatures Cassiopeia [0.95] You Features] [0.54] For a ForLess] [0.52] EM-500 could get some similar lower price of around In contrast to this (Yellow) features to the Casio $295, the Palm OS- Casio model, you Cassiopeia EM-500, based Palm can enjoy certain but at a lower price of Tungsten T would advantages (while around $135, in the give you similar getting a lower Palm OS-based Sony features to the Casio price) in the Palm CLIÉ PEG-SJ20. Just Cassiopeia EM-500. Tungsten T3. To like the Casio model, Just like the Casio, begin with, it has the Sony has 16 MB the Palm has 16 MB 64 MB memory, memory. And note that memory and has a which is larger it has a 320 × 320 16-bit, 64,000-color than the Casio screen resolution, display. Cassiopeia EM- which is fairly 500 (16 MB). comparable to the Moreover, the 240 × 320 of the Casio Palm Tungsten T3 model. typically costs about $400, compared to $500 for the Casio Cassiopeia EM- 500. HP Palmtop [CompBetterFeatures [CompBetterFeatures [AltSameFeatures [AltCrossUpSell] PC 320 LX ForLess] [1.07] To ForLess] [0.66] To AndPrice] [0.45] If [0.25] Before you help decide if the HP help decide if the HP you are interested decide to buy the Palmtop PC 320 is Palmtop PC 320 is in a multimedia HP Palmtop PC your best choice in a your best choice in a handheld with 320, you might handheld, you may handheld, you might many similar want to compare want to check out the want to take a look at features to the HP it to the Sony Palm Zire, which will the Sony CLIÉ PEG- Palmtop PC 320, CLIÉ PEG-UX50, give you some S360, as it will and for around the a multimedia advantages at a lower provide you certain same cost, then handheld which price than the HP additional benefits at check out the has certain Palmtop PC 320. For a lower price than the Sony CLIÉ PEG- advantages. For example, it weighs just HP Palmtop PC 320. UX4O. It sells for example, it has 3.8 ounces, which is For example, it around $470. 40 MB memory, relatively lighter than weighs just 4.2 Similar in many of against the 4 MB the HP Palmtop PC ounces, which is its features, the of the HP 320 (15.6 ounces). relatively lighter than Sony has a Palmtop PC 320. The weight of a the HP Palmtop PC 480 × 320 screen In addition, the handheld is significant 320 (15.6 ounces). resolution, which Sony CLIÉ PEG- for those who will be The weight of a is similar to the UX50 weighs just holding it for lengthy handheld is 640 × 240 of the HP 6.2 ounces, as periods of time and significant for those model, and it also opposed to 15.6 may be subject to who will be holding it is just 3.5 inches ounces for the hand and finger for lengthy periods of in length, which HP Palmtop PC fatigue. The Palm Zire time and may be again is similar to 320. The weight also has a mere 2.9 subject to hand and the 3.7 inches of of a handheld is inch width, which is finger fatigue. The the HP model. The significant for narrower than the HP Sony CLIÉ PEG- higher resolution those who will be Palmtop PC 320 (7.2 S360 furthermore has will come in handy holding it for inch). The size of the a mere 2.9 inch if you want to use lengthy periods of device can be a width, which is your handheld as time and may be significant factor, for narrower than the HP a viewer for subject to hand example, if you intend Palmtop PC 320 (7.2 photos from a and finger to carry the device in inch). The size of the digital camera. fatigue. Also, its your shirt pocket or device can be a $555 street price pants pocket. significant factor, for is only $85 more Moreover, the Palm example, if you than the HP Zire typically costs intend to carry the Palmtop PC 320. around $70, compared device in your shirt to $470 for the HP pocket or pants Palmtop PC 320. pocket. Moreover, the Sony CLIÉ PEG- S360 typically costs around $180, as opposed to $470 for the HP Palmtop PC 320. 

1. A computer-implemented method of automatically generating a self-updating naturally-reading narrative product summary, the method comprising: determining if a narrative product summary exists for a selected product; and if a narrative product summary exists for a product, reconciling the existing narrative product summary into an existing attribute associated with the selected product, the existing attribute including at least one of an existing attribute name, an existing attribute value, or an existing attribute unit; and resolving defined forms in which an assertion describes the selected product to an existing assertion model; then comparing at least one of the existing attribute name, the existing attribute value, the existing attribute unit, and the existing assertion model, respectively, to at least one of a current attribute name, a current attribute value, a current attribute unit, and a current assertion model to determine if at least one of the comparisons shows a change in the attribute name, the attribute value, the attribute unit, or the assertion model; and if a change is evident; determining at least one new attribute associated with the selected product, the at least one new attribute including at least one of a changed attribute name, a changed attribute value, or a changed attribute unit; retrieving a new assertion model defining changed forms in which a new assertion is used to describe the product in a natural manner; and generating an updated naturally reading narrative by combining the new attribute with the retrieved new assertion model.
 2. A computer-implemented method of automatically generating a self-updating naturally-reading narrative product summary about a selected product, the method comprising: evaluating an existing narrative product summary including: reconciling the existing narrative product summary into an existing attribute associated with the selected product, the existing attribute including at least one of an existing attribute name, an existing attribute value, or an existing attribute unit, and resolving forms in the existing narrative product summary to an existing assertion model; comparing at least one of the existing attribute name, the existing attribute value, the existing attribute unit, and the existing assertion model, respectively, to at least one of a current attribute name, a current attribute value, a current attribute unit, and a current assertion model to determine if at least one of the comparisons shows a change in the attribute name, the attribute value, the attribute unit, or the assertion model; determining at least one new attribute associated with the selected product, the at least one new attribute including at least one of a changed attribute name, a changed attribute value, or a changed attribute unit; selecting an alternative product; retrieving new assertion models defining forms in which assertions describe the selected product and identify the alternative product in a natural manner; and generating a naturally-reading narrative product summary by combining the at least one new attribute with a new retrieved assertion model, and combining the selected alternative product with another retrieved assertion model to recommend the selected alternative product in the narrative.
 3. The method of claim 2, wherein the at least one new attribute associated with the selected product includes an indication of the length of time the selected product has been offered for sale.
 4. The method of claim 2, wherein the new assertion models define forms in which the assertions include an indication of the length of time the selected product has been offered for sale.
 5. The method of claim 2, wherein the existing attribute value of the selected product is ordered within an evaluative scale of alternative products.
 6. The method of claim 5, wherein the existing attribute value of the selected product ordered within the evaluative scale of alternative products changes over time with respect to the alternative products.
 7. The method of claim 2, further comprising notifying a user that an update has taken place of the self-updating naturally-reading narrative product summary.
 8. The method of claim 7, wherein the user is notified by email or RSS feed that an update has taken place of the self-updating naturally-reading narrative product summary.
 9. The method of claim 2, wherein the selected alternative product is selected from a product category of a class of peer products.
 10. The method of claim 9, wherein characteristics of peer products in the class of peer products cause a change in a scalarization range of the existing attribute value of the selected product, thereby introducing a new assertion based upon a changed relative position of the selected product in the peer class.
 11. The method of claim 10, wherein the characteristics of peer products in the class of peer products includes the number or type of peer products.
 12. The method of claim 11, wherein the characteristic of number or type of peer product in the class of peer products causes a change in dollar value of the selected product.
 13. The method of claim 2, wherein the selected alternative product is ranked within a predetermined near-rank margin of the selected product.
 14. The method of claim 2, wherein the selected alternative product has a price that is substantially the same as a price of the selected product.
 15. The method of claim 2, wherein the selected alternative product has a price within a predetermined near-price margin of the selected product.
 16. The method of claim 2, wherein the selected alternative product and the selected product have the same primary scenario.
 17. The method of claim 2, wherein the recommendation is an advertisement, and further including the step of receiving compensation in exchange for recommending the selected alternative product.
 18. The method of claim 17, wherein the amount of compensation is based on at least one of frequency of the recommendation and the number of recommended products sold that is attributable to the recommendation.
 19. The method of claim 2, wherein the assertion models include a plurality of assertion templates, each assertion template including a natural sentence pattern and at least one field corresponding to the at least one attribute.
 20. The method of claim 19, further including the step of assigning importance ratings to each of the at least one attributes.
 21. The method of claim 20, further including the step of determining attribute ranks for each of the at least one attributes, and processing the attribute ranks together with the importance ratings to derive a severity value for each of the at least one attributes.
 22. The method of claim 21, wherein the alternative product has the same primary scenario as the selected product and is selected based on at least one of a predetermined near-price margin, a predetermined near-rank margin, and a predetermined severity differential threshold.
 23. A product summary generator for automatically generating a self-updating naturally-reading narrative product summary about a selected product, the product summary generator comprising: a summary generation module adapted to evaluate an existing narrative product summary including: reconciling the existing narrative product summary into an existing attribute associated with the selected product, the existing attribute including at least one of an existing attribute name, an existing attribute value, or an existing attribute unit, and resolving forms in the existing narrative product summary to an existing assertion model; an engine adapted to compare at least one of the existing attribute name, the existing attribute value, the existing attribute unit, and the existing assertion model, respectively, to at least one of a current attribute name, a current attribute value, a current attribute unit, and a current assertion model to determine if at least one of the comparisons shows a change in the attribute name, the attribute value, the attribute unit, or the assertion model; a product attribute module adapted to obtain at least one new attribute associated with the selected product, the at least one new attribute including at least one of a changed attribute name, a changed attribute value, or a changed attribute unit, an alternative product selection module adapted to select an alternative product; an assertion model module adapted to retrieve new assertion models that define forms in which assertions describe the selected product and in which assertions describe the selected alternative product in a natural manner; and the summary generation module further adapted to combine the at least one new attribute with a new retrieved assertion model to describe the selected product in the narrative and to combine the selected alternative product with another retrieved assertion model to recommend the selected alternative product in the narrative.
 24. The product summary generator of claim 23, wherein the recommendation made by the summary generation module is an advertisement for which compensation is received in exchange for recommending the selected alternative product.
 25. The product summary generator of claim 24, wherein the amount of compensation is based on at least one of frequency of the recommendation and number of recommended products sold that is attributable to the recommendation.
 26. The product summary generator of claim 23, further comprising an alternative products database adapted to store a plurality of candidate alternative products and wherein the alternative product selection module selects the alternative product from the alternative products database.
 27. A method of automatically generating a self-updating naturally-reading narrative product summary including assertions about a selected product, the method comprising: evaluating an existing narrative product summary including: reconciling the existing narrative product summary into an existing attribute associated with the selected product, the existing attribute including at least one of an existing attribute name, an existing attribute value, and an existing attribute unit, and resolving forms in the existing narrative product summary into an existing assertion model; comparing at least one of the existing attribute name, the existing attribute value, the existing attribute unit, and the existing assertion model, respectively, to at least one of a current attribute name, a current attribute value, a current attribute unit, and a current assertion model to determine if at least one of the comparisons shows a change in the attribute name, the attribute value, the attribute unit, or the assertion model; determining at least one new attribute associated with the selected product, the at least one attribute including at least one of a changed attribute name, a changed attribute value, and a changed attribute unit; retrieving assertion models defining forms in which an assertion can be manifested to describe the selected product, and to recommend an alternative product in a natural manner; receiving a plurality of bids from manufacturers that indicate an amount of compensation each manufacturer is willing to pay to have at least one of the manufacturer's product be recommended as the alternative product; selecting the alternative product based on the plurality of bids; and generating an updated naturally reading narrative by combining the at least one new attribute and the selected alternative product with the retrieved assertion models such that the generated narrative includes a recommendation of the selected alternative product.
 28. The method of claim 27, wherein the amount of the compensation is based on at least one of frequency of the recommendation and number of recommended products sold that is attributable to the recommendation.
 29. The method of claim 27, wherein the existing attribute value of the selected product is ordered within an evaluative scale of alternative products.
 30. The method of claim 29, wherein the existing attribute value of the selected product ordered within the evaluative scale of alternative products changes over time with respect to the alternative products.
 31. The method of claim 27, further comprising notifying a user that an update has taken place of the self-updating naturally-reading narrative product summary.
 32. The method of claim 31, wherein the user is notified by email or RSS feed that an update has taken place of the self-updating naturally-reading narrative product summary.
 33. The method of claim 27, wherein the selected alternative product is selected from a product category of a class of peer products.
 34. The method of claim 33, wherein characteristics of peer products in the class of peer products cause a change in a scalarization range of the existing attribute value of the selected product, thereby introducing a new assertion based upon a changed relative position of the selected product in the peer class.
 35. The method of claim 34, wherein the characteristics of peer products in the class of peer products includes the number or type of peer products.
 36. The method of claim 35, wherein the characteristic of number or type of peer product in the class of peer products causes a change in dollar value of the selected product. 